Streaming messages in the Model Context Protocol delivers measurable performance gains, but the implementation complexity isn't trivial. At Stainless, we've seen teams struggle with the decision of when streaming justifies the engineering overhead versus sticking with simpler batch responses.
This article breaks down the specific performance characteristics of MCP streaming, compares transport options like Streamable HTTP versus Server-Sent Events, and provides concrete guidance on when real engineering teams choose streaming over batch responses. You'll learn how to benchmark your MCP server, understand the complexity trade-offs, and make informed decisions about streaming implementation for your specific use case.
What performance gains come from streaming messages?
Streaming messages in the Model Context Protocol (MCP) significantly reduces the time it takes for a client to see the first piece of data and lowers server memory usage, especially for large payloads. As adoption accelerates and MCP is eating the world, understanding these performance characteristics becomes crucial for developers building efficient AI integrations. Instead of waiting for a server to prepare a massive response all at once, streaming sends the data in manageable chunks as it becomes available. This makes applications feel faster and allows servers to handle more users simultaneously.
This approach fundamentally changes the request-response dynamic. It moves from a "wait for it all" model to a "get it as it comes" flow, which has direct, measurable benefits for both latency and resource management.
Latency advantage
The most immediate benefit of streaming is a dramatic improvement in perceived latency, often measured as Time to First Byte (TTFB). With a traditional batch response, if a request generates a 10MB JSON payload, the user waits for the entire 10MB to be generated, serialized, and sent before seeing anything. With streaming, the client receives the first chunk of data almost instantly, allowing the UI to start rendering results while the rest of the data arrives in the background.
Memory footprint
Streaming is incredibly efficient for server memory. A batch response requires the server to hold the entire payload in memory before sending it, which can be a huge resource drain. A streaming server, on the other hand, can process and send data in smaller pieces, releasing memory as it goes. This prevents memory spikes and allows a single server to operate smoothly even when handling very large data responses.
Concurrency throughput
By reducing the memory required for each request, streaming directly increases a server's capacity to handle more concurrent connections. When each connection consumes less memory, the server can serve more clients at the same time without becoming overloaded or needing to scale up hardware. This higher throughput is essential for building scalable, production-grade services on top of MCP.
Where does streaming beat batch responses?
Deciding between streaming and batch responses is not always a clear-cut choice; it depends entirely on your API's specific workflow and data patterns. While streaming offers powerful advantages, batching still has its place. The key is to identify the scenarios where the benefits of streaming provide a distinctly better experience.
Large payload threshold
Streaming becomes the obvious choice when dealing with large data payloads. A common rule of thumb is to consider streaming for any response larger than 1MB. Endpoints that return long lists of objects, detailed documents, or raw file content are prime candidates. For these use cases, batching would introduce unacceptable latency and server strain.
Real-time data feeds
For any application that relies on real-time data, streaming is the only practical solution. This includes use cases like:
Live activity logs
Real-time monitoring and metrics dashboards
Event-driven notification systems
Continuous data pipelines
In these scenarios, data is generated continuously, and a batch model would be completely ineffective. Streaming provides the constant, low-latency flow of information that these applications require.
Small request edge cases
It is important to be honest about where streaming is overkill. For small, predictable responses, like fetching a single user profile or a configuration object, a simple batch response is often more efficient. The overhead of establishing and managing a stream can introduce a slight delay that outweighs any benefits for tiny payloads. In these cases, sticking with a traditional request-response model is simpler and faster.
How much complexity does streaming introduce?
While the performance gains are compelling, they come with a trade-off: increased complexity in both the server and client code. Adopting a streaming model requires a different way of thinking about data flow and error handling. Understanding this complexity is key to deciding if the performance benefits are worth the engineering effort for your specific use case.
Server code delta
On the server, implementing a streaming endpoint is more involved than a simple batch response. Instead of just returning a complete data structure, the code must be designed to iterate over a data source, serialize it into chunks, and write those chunks to an output stream. Modern language features like async generators in TypeScript and Python make this more manageable, and tools that generate an MCP server from an OpenAPI spec can abstract away much of this boilerplate.
Client handling logic
The client also takes on more responsibility. Instead of a simple await response.json(), the client code must be prepared to read from a stream, process incoming chunks as they arrive, and potentially stitch them back together or update a UI progressively. This asynchronous logic is inherently more complex to write, debug, and maintain.
Error recovery paths
Error handling is significantly more challenging with streaming. If a connection drops halfway through a 10MB batch response, the client simply retries the entire request. If it drops mid-stream, what happens?
Partial Data: The client is left with incomplete data. Does it discard it or try to use it?
Resumption: Can the stream be resumed from where it left off? This requires complex state management on both the client and server.
Idempotency: How do you prevent duplicate processing if a retry is attempted?
These challenges require careful design and robust engineering to solve effectively.
Which transport offers the best trade-off?
The Model Context Protocol is transport-agnostic, but for streaming messages over the web, two primary methods have emerged. The choice between them often comes down to balancing modern capabilities against simplicity and legacy support.
Feature | Streamable HTTP | Server-Sent Events (SSE) |
|---|---|---|
Directionality | Bidirectional | Unidirectional (Server to Client) |
Connection | Single, long-lived HTTP connection | Standard HTTP connection |
Complexity | More complex to implement | Simpler, well-established standard |
Use Case | Interactive, two-way communication | Notifications, real-time updates |
Modern Support | The modern, recommended approach | Legacy support, some proxy issues |
Streamable HTTP metrics
Streamable HTTP is the modern, flexible transport that uses a single HTTP connection for both client-to-server requests and server-to-client streaming. This bidirectional capability makes it highly efficient for interactive applications where both sides need to send messages continuously. It is the recommended approach for new, complex MCP implementations and is often the default for remote servers deployed on platforms like Cloudflare Workers.
SSE limitations
Server-Sent Events (SSE) is an older, simpler standard that excels at one thing: pushing data from the server to the client. It is perfect for notifications or simple data feeds. However, it is strictly unidirectional. If the client needs to send messages back to the server after the initial connection, it must do so over a separate HTTP request, which adds complexity and overhead.
Client capability impact
Ultimately, the best transport can depend on the clients you need to support. Different MCP clients, like Claude Desktop or Cursor, may have varying levels of support for different transports and schema features. A well-designed MCP server can even detect the capabilities of a connected client and adapt its communication strategy on the fly, ensuring maximum compatibility across the ecosystem.
When do real teams choose streaming?
Moving from theory to practice, streaming is not just an academic exercise. Engineering teams are actively using it to solve real-world problems and build more powerful, responsive applications with AI. The transition from API to MCP often involves implementing these streaming patterns to maximize performance and user experience. Here are a few common patterns where streaming is the clear winner.
LLM context expansion
When an LLM agent needs to analyze a large corpus of information—like an entire codebase, a lengthy PDF, or extensive documentation—streaming is essential. Instead of trying to load a massive file into a single prompt, which can hit token limits or cause timeouts, the data can be streamed to the agent. This allows the LLM to begin processing the information piece by piece, making it possible to work with contexts that would be too large for a single batch request.
Log and metrics pipelines
This is a classic data engineering use case. Applications, servers, and services generate a constant stream of log and metric data. Streaming provides a reliable and efficient pipeline to transport this high-volume, continuous data to central observability platforms for analysis, alerting, and dashboarding.
Progressive analytics queries
In data analytics and business intelligence tools, running a complex query against a large dataset can take a significant amount of time. Instead of forcing a user to stare at a loading spinner for minutes, a streaming response can progressively populate charts and tables as results are computed. This provides a much better user experience, giving immediate feedback and allowing the user to see trends emerge in real time.
Frequently asked questions about MCP streaming performance
What response size triggers streaming benefits?
A good rule of thumb is to consider streaming for any response over 1MB, but for real-time data feeds, even small messages benefit from a streaming architecture to ensure low latency.
How do I benchmark my MCP server?
Focus on Time to First Byte (TTFB) to measure initial responsiveness and monitor the time between chunks to gauge throughput, while also tracking server-side memory usage under load.
Which clients fully support streaming today?
Major clients like Claude Desktop, OpenAI's agent platform, and Cursor have robust and growing support for streaming, but it is always best to test against your specific target clients as capabilities can differ.
Can I mix batch and streaming in a single server?
Yes, and this is a highly recommended practice; when creating an MCP server from an OpenAPI spec, you can configure it to use streaming for data-intensive list endpoints while using simple batch responses for fetching single, small items.
What overhead comes from client capability transforms?
Dynamically transforming schemas for less capable clients can add a 10-20% latency overhead, which can be mitigated by designing schemas for broad compatibility from the start where possible. The experience gained from converting complex OpenAPI specs to MCP servers shows how schema design choices directly impact client compatibility and performance.
Ready to build a high-performance MCP server for your API? Get started for free.
Streaming messages in the Model Context Protocol delivers measurable performance gains, but the implementation complexity isn't trivial. At Stainless, we've seen teams struggle with the decision of when streaming justifies the engineering overhead versus sticking with simpler batch responses.
This article breaks down the specific performance characteristics of MCP streaming, compares transport options like Streamable HTTP versus Server-Sent Events, and provides concrete guidance on when real engineering teams choose streaming over batch responses. You'll learn how to benchmark your MCP server, understand the complexity trade-offs, and make informed decisions about streaming implementation for your specific use case.
What performance gains come from streaming messages?
Streaming messages in the Model Context Protocol (MCP) significantly reduces the time it takes for a client to see the first piece of data and lowers server memory usage, especially for large payloads. As adoption accelerates and MCP is eating the world, understanding these performance characteristics becomes crucial for developers building efficient AI integrations. Instead of waiting for a server to prepare a massive response all at once, streaming sends the data in manageable chunks as it becomes available. This makes applications feel faster and allows servers to handle more users simultaneously.
This approach fundamentally changes the request-response dynamic. It moves from a "wait for it all" model to a "get it as it comes" flow, which has direct, measurable benefits for both latency and resource management.
Latency advantage
The most immediate benefit of streaming is a dramatic improvement in perceived latency, often measured as Time to First Byte (TTFB). With a traditional batch response, if a request generates a 10MB JSON payload, the user waits for the entire 10MB to be generated, serialized, and sent before seeing anything. With streaming, the client receives the first chunk of data almost instantly, allowing the UI to start rendering results while the rest of the data arrives in the background.
Memory footprint
Streaming is incredibly efficient for server memory. A batch response requires the server to hold the entire payload in memory before sending it, which can be a huge resource drain. A streaming server, on the other hand, can process and send data in smaller pieces, releasing memory as it goes. This prevents memory spikes and allows a single server to operate smoothly even when handling very large data responses.
Concurrency throughput
By reducing the memory required for each request, streaming directly increases a server's capacity to handle more concurrent connections. When each connection consumes less memory, the server can serve more clients at the same time without becoming overloaded or needing to scale up hardware. This higher throughput is essential for building scalable, production-grade services on top of MCP.
Where does streaming beat batch responses?
Deciding between streaming and batch responses is not always a clear-cut choice; it depends entirely on your API's specific workflow and data patterns. While streaming offers powerful advantages, batching still has its place. The key is to identify the scenarios where the benefits of streaming provide a distinctly better experience.
Large payload threshold
Streaming becomes the obvious choice when dealing with large data payloads. A common rule of thumb is to consider streaming for any response larger than 1MB. Endpoints that return long lists of objects, detailed documents, or raw file content are prime candidates. For these use cases, batching would introduce unacceptable latency and server strain.
Real-time data feeds
For any application that relies on real-time data, streaming is the only practical solution. This includes use cases like:
Live activity logs
Real-time monitoring and metrics dashboards
Event-driven notification systems
Continuous data pipelines
In these scenarios, data is generated continuously, and a batch model would be completely ineffective. Streaming provides the constant, low-latency flow of information that these applications require.
Small request edge cases
It is important to be honest about where streaming is overkill. For small, predictable responses, like fetching a single user profile or a configuration object, a simple batch response is often more efficient. The overhead of establishing and managing a stream can introduce a slight delay that outweighs any benefits for tiny payloads. In these cases, sticking with a traditional request-response model is simpler and faster.
How much complexity does streaming introduce?
While the performance gains are compelling, they come with a trade-off: increased complexity in both the server and client code. Adopting a streaming model requires a different way of thinking about data flow and error handling. Understanding this complexity is key to deciding if the performance benefits are worth the engineering effort for your specific use case.
Server code delta
On the server, implementing a streaming endpoint is more involved than a simple batch response. Instead of just returning a complete data structure, the code must be designed to iterate over a data source, serialize it into chunks, and write those chunks to an output stream. Modern language features like async generators in TypeScript and Python make this more manageable, and tools that generate an MCP server from an OpenAPI spec can abstract away much of this boilerplate.
Client handling logic
The client also takes on more responsibility. Instead of a simple await response.json(), the client code must be prepared to read from a stream, process incoming chunks as they arrive, and potentially stitch them back together or update a UI progressively. This asynchronous logic is inherently more complex to write, debug, and maintain.
Error recovery paths
Error handling is significantly more challenging with streaming. If a connection drops halfway through a 10MB batch response, the client simply retries the entire request. If it drops mid-stream, what happens?
Partial Data: The client is left with incomplete data. Does it discard it or try to use it?
Resumption: Can the stream be resumed from where it left off? This requires complex state management on both the client and server.
Idempotency: How do you prevent duplicate processing if a retry is attempted?
These challenges require careful design and robust engineering to solve effectively.
Which transport offers the best trade-off?
The Model Context Protocol is transport-agnostic, but for streaming messages over the web, two primary methods have emerged. The choice between them often comes down to balancing modern capabilities against simplicity and legacy support.
Feature | Streamable HTTP | Server-Sent Events (SSE) |
|---|---|---|
Directionality | Bidirectional | Unidirectional (Server to Client) |
Connection | Single, long-lived HTTP connection | Standard HTTP connection |
Complexity | More complex to implement | Simpler, well-established standard |
Use Case | Interactive, two-way communication | Notifications, real-time updates |
Modern Support | The modern, recommended approach | Legacy support, some proxy issues |
Streamable HTTP metrics
Streamable HTTP is the modern, flexible transport that uses a single HTTP connection for both client-to-server requests and server-to-client streaming. This bidirectional capability makes it highly efficient for interactive applications where both sides need to send messages continuously. It is the recommended approach for new, complex MCP implementations and is often the default for remote servers deployed on platforms like Cloudflare Workers.
SSE limitations
Server-Sent Events (SSE) is an older, simpler standard that excels at one thing: pushing data from the server to the client. It is perfect for notifications or simple data feeds. However, it is strictly unidirectional. If the client needs to send messages back to the server after the initial connection, it must do so over a separate HTTP request, which adds complexity and overhead.
Client capability impact
Ultimately, the best transport can depend on the clients you need to support. Different MCP clients, like Claude Desktop or Cursor, may have varying levels of support for different transports and schema features. A well-designed MCP server can even detect the capabilities of a connected client and adapt its communication strategy on the fly, ensuring maximum compatibility across the ecosystem.
When do real teams choose streaming?
Moving from theory to practice, streaming is not just an academic exercise. Engineering teams are actively using it to solve real-world problems and build more powerful, responsive applications with AI. The transition from API to MCP often involves implementing these streaming patterns to maximize performance and user experience. Here are a few common patterns where streaming is the clear winner.
LLM context expansion
When an LLM agent needs to analyze a large corpus of information—like an entire codebase, a lengthy PDF, or extensive documentation—streaming is essential. Instead of trying to load a massive file into a single prompt, which can hit token limits or cause timeouts, the data can be streamed to the agent. This allows the LLM to begin processing the information piece by piece, making it possible to work with contexts that would be too large for a single batch request.
Log and metrics pipelines
This is a classic data engineering use case. Applications, servers, and services generate a constant stream of log and metric data. Streaming provides a reliable and efficient pipeline to transport this high-volume, continuous data to central observability platforms for analysis, alerting, and dashboarding.
Progressive analytics queries
In data analytics and business intelligence tools, running a complex query against a large dataset can take a significant amount of time. Instead of forcing a user to stare at a loading spinner for minutes, a streaming response can progressively populate charts and tables as results are computed. This provides a much better user experience, giving immediate feedback and allowing the user to see trends emerge in real time.
Frequently asked questions about MCP streaming performance
What response size triggers streaming benefits?
A good rule of thumb is to consider streaming for any response over 1MB, but for real-time data feeds, even small messages benefit from a streaming architecture to ensure low latency.
How do I benchmark my MCP server?
Focus on Time to First Byte (TTFB) to measure initial responsiveness and monitor the time between chunks to gauge throughput, while also tracking server-side memory usage under load.
Which clients fully support streaming today?
Major clients like Claude Desktop, OpenAI's agent platform, and Cursor have robust and growing support for streaming, but it is always best to test against your specific target clients as capabilities can differ.
Can I mix batch and streaming in a single server?
Yes, and this is a highly recommended practice; when creating an MCP server from an OpenAPI spec, you can configure it to use streaming for data-intensive list endpoints while using simple batch responses for fetching single, small items.
What overhead comes from client capability transforms?
Dynamically transforming schemas for less capable clients can add a 10-20% latency overhead, which can be mitigated by designing schemas for broad compatibility from the start where possible. The experience gained from converting complex OpenAPI specs to MCP servers shows how schema design choices directly impact client compatibility and performance.
Ready to build a high-performance MCP server for your API? Get started for free.
Streaming messages in the Model Context Protocol delivers measurable performance gains, but the implementation complexity isn't trivial. At Stainless, we've seen teams struggle with the decision of when streaming justifies the engineering overhead versus sticking with simpler batch responses.
This article breaks down the specific performance characteristics of MCP streaming, compares transport options like Streamable HTTP versus Server-Sent Events, and provides concrete guidance on when real engineering teams choose streaming over batch responses. You'll learn how to benchmark your MCP server, understand the complexity trade-offs, and make informed decisions about streaming implementation for your specific use case.
What performance gains come from streaming messages?
Streaming messages in the Model Context Protocol (MCP) significantly reduces the time it takes for a client to see the first piece of data and lowers server memory usage, especially for large payloads. As adoption accelerates and MCP is eating the world, understanding these performance characteristics becomes crucial for developers building efficient AI integrations. Instead of waiting for a server to prepare a massive response all at once, streaming sends the data in manageable chunks as it becomes available. This makes applications feel faster and allows servers to handle more users simultaneously.
This approach fundamentally changes the request-response dynamic. It moves from a "wait for it all" model to a "get it as it comes" flow, which has direct, measurable benefits for both latency and resource management.
Latency advantage
The most immediate benefit of streaming is a dramatic improvement in perceived latency, often measured as Time to First Byte (TTFB). With a traditional batch response, if a request generates a 10MB JSON payload, the user waits for the entire 10MB to be generated, serialized, and sent before seeing anything. With streaming, the client receives the first chunk of data almost instantly, allowing the UI to start rendering results while the rest of the data arrives in the background.
Memory footprint
Streaming is incredibly efficient for server memory. A batch response requires the server to hold the entire payload in memory before sending it, which can be a huge resource drain. A streaming server, on the other hand, can process and send data in smaller pieces, releasing memory as it goes. This prevents memory spikes and allows a single server to operate smoothly even when handling very large data responses.
Concurrency throughput
By reducing the memory required for each request, streaming directly increases a server's capacity to handle more concurrent connections. When each connection consumes less memory, the server can serve more clients at the same time without becoming overloaded or needing to scale up hardware. This higher throughput is essential for building scalable, production-grade services on top of MCP.
Where does streaming beat batch responses?
Deciding between streaming and batch responses is not always a clear-cut choice; it depends entirely on your API's specific workflow and data patterns. While streaming offers powerful advantages, batching still has its place. The key is to identify the scenarios where the benefits of streaming provide a distinctly better experience.
Large payload threshold
Streaming becomes the obvious choice when dealing with large data payloads. A common rule of thumb is to consider streaming for any response larger than 1MB. Endpoints that return long lists of objects, detailed documents, or raw file content are prime candidates. For these use cases, batching would introduce unacceptable latency and server strain.
Real-time data feeds
For any application that relies on real-time data, streaming is the only practical solution. This includes use cases like:
Live activity logs
Real-time monitoring and metrics dashboards
Event-driven notification systems
Continuous data pipelines
In these scenarios, data is generated continuously, and a batch model would be completely ineffective. Streaming provides the constant, low-latency flow of information that these applications require.
Small request edge cases
It is important to be honest about where streaming is overkill. For small, predictable responses, like fetching a single user profile or a configuration object, a simple batch response is often more efficient. The overhead of establishing and managing a stream can introduce a slight delay that outweighs any benefits for tiny payloads. In these cases, sticking with a traditional request-response model is simpler and faster.
How much complexity does streaming introduce?
While the performance gains are compelling, they come with a trade-off: increased complexity in both the server and client code. Adopting a streaming model requires a different way of thinking about data flow and error handling. Understanding this complexity is key to deciding if the performance benefits are worth the engineering effort for your specific use case.
Server code delta
On the server, implementing a streaming endpoint is more involved than a simple batch response. Instead of just returning a complete data structure, the code must be designed to iterate over a data source, serialize it into chunks, and write those chunks to an output stream. Modern language features like async generators in TypeScript and Python make this more manageable, and tools that generate an MCP server from an OpenAPI spec can abstract away much of this boilerplate.
Client handling logic
The client also takes on more responsibility. Instead of a simple await response.json(), the client code must be prepared to read from a stream, process incoming chunks as they arrive, and potentially stitch them back together or update a UI progressively. This asynchronous logic is inherently more complex to write, debug, and maintain.
Error recovery paths
Error handling is significantly more challenging with streaming. If a connection drops halfway through a 10MB batch response, the client simply retries the entire request. If it drops mid-stream, what happens?
Partial Data: The client is left with incomplete data. Does it discard it or try to use it?
Resumption: Can the stream be resumed from where it left off? This requires complex state management on both the client and server.
Idempotency: How do you prevent duplicate processing if a retry is attempted?
These challenges require careful design and robust engineering to solve effectively.
Which transport offers the best trade-off?
The Model Context Protocol is transport-agnostic, but for streaming messages over the web, two primary methods have emerged. The choice between them often comes down to balancing modern capabilities against simplicity and legacy support.
Feature | Streamable HTTP | Server-Sent Events (SSE) |
|---|---|---|
Directionality | Bidirectional | Unidirectional (Server to Client) |
Connection | Single, long-lived HTTP connection | Standard HTTP connection |
Complexity | More complex to implement | Simpler, well-established standard |
Use Case | Interactive, two-way communication | Notifications, real-time updates |
Modern Support | The modern, recommended approach | Legacy support, some proxy issues |
Streamable HTTP metrics
Streamable HTTP is the modern, flexible transport that uses a single HTTP connection for both client-to-server requests and server-to-client streaming. This bidirectional capability makes it highly efficient for interactive applications where both sides need to send messages continuously. It is the recommended approach for new, complex MCP implementations and is often the default for remote servers deployed on platforms like Cloudflare Workers.
SSE limitations
Server-Sent Events (SSE) is an older, simpler standard that excels at one thing: pushing data from the server to the client. It is perfect for notifications or simple data feeds. However, it is strictly unidirectional. If the client needs to send messages back to the server after the initial connection, it must do so over a separate HTTP request, which adds complexity and overhead.
Client capability impact
Ultimately, the best transport can depend on the clients you need to support. Different MCP clients, like Claude Desktop or Cursor, may have varying levels of support for different transports and schema features. A well-designed MCP server can even detect the capabilities of a connected client and adapt its communication strategy on the fly, ensuring maximum compatibility across the ecosystem.
When do real teams choose streaming?
Moving from theory to practice, streaming is not just an academic exercise. Engineering teams are actively using it to solve real-world problems and build more powerful, responsive applications with AI. The transition from API to MCP often involves implementing these streaming patterns to maximize performance and user experience. Here are a few common patterns where streaming is the clear winner.
LLM context expansion
When an LLM agent needs to analyze a large corpus of information—like an entire codebase, a lengthy PDF, or extensive documentation—streaming is essential. Instead of trying to load a massive file into a single prompt, which can hit token limits or cause timeouts, the data can be streamed to the agent. This allows the LLM to begin processing the information piece by piece, making it possible to work with contexts that would be too large for a single batch request.
Log and metrics pipelines
This is a classic data engineering use case. Applications, servers, and services generate a constant stream of log and metric data. Streaming provides a reliable and efficient pipeline to transport this high-volume, continuous data to central observability platforms for analysis, alerting, and dashboarding.
Progressive analytics queries
In data analytics and business intelligence tools, running a complex query against a large dataset can take a significant amount of time. Instead of forcing a user to stare at a loading spinner for minutes, a streaming response can progressively populate charts and tables as results are computed. This provides a much better user experience, giving immediate feedback and allowing the user to see trends emerge in real time.
Frequently asked questions about MCP streaming performance
What response size triggers streaming benefits?
A good rule of thumb is to consider streaming for any response over 1MB, but for real-time data feeds, even small messages benefit from a streaming architecture to ensure low latency.
How do I benchmark my MCP server?
Focus on Time to First Byte (TTFB) to measure initial responsiveness and monitor the time between chunks to gauge throughput, while also tracking server-side memory usage under load.
Which clients fully support streaming today?
Major clients like Claude Desktop, OpenAI's agent platform, and Cursor have robust and growing support for streaming, but it is always best to test against your specific target clients as capabilities can differ.
Can I mix batch and streaming in a single server?
Yes, and this is a highly recommended practice; when creating an MCP server from an OpenAPI spec, you can configure it to use streaming for data-intensive list endpoints while using simple batch responses for fetching single, small items.
What overhead comes from client capability transforms?
Dynamically transforming schemas for less capable clients can add a 10-20% latency overhead, which can be mitigated by designing schemas for broad compatibility from the start where possible. The experience gained from converting complex OpenAPI specs to MCP servers shows how schema design choices directly impact client compatibility and performance.
Ready to build a high-performance MCP server for your API? Get started for free.