10 Best MCP Servers For Developers

We work with a wide range of APIs and developer tools. Over the past year, our team has been integrating Model Context Protocol (MCP) servers into our internal workflows.

How We Selected The Top 10 MCP Servers

To identify the best MCP servers for this list, we evaluated them based on actual implementations at Stainless. We tested each server with different API types and programming languages between February and June 2025.

Our evaluation focused on four main criteria:

• Ease of implementation: How quickly we could set up and configure the server

• Documentation quality: Whether it had clear, complete documentation with examples

• Performance: Response time, memory usage, and request throughput

• Community support: Active maintenance and available support resources

We excluded servers that failed basic interoperability checks or added too much overhead. The awesome MCP servers we selected had to be reliable, well-documented, and actively maintained.

10 Best MCP Servers That Transformed Our Development Process

1. AWS Serverless MCP

AWS Serverless MCP uses Lambda and API Gateway to run MCP tools without managing servers. It scales automatically based on demand.

This setup worked well for deploying MCP endpoints across regions with minimal configuration. We could focus on the tool logic instead of infrastructure.

import boto3
def lambda_handler(event, context):
    # MCP logic here
    return {"statusCode": 200, "body": "MCP response"}

Key features:

• Auto-scaling based on request volume

• Pay-per-use billing (no idle costs)

• Built-in integration with other AWS services

The main limitation is that you need to work within AWS-specific patterns, and cold starts can add latency to infrequent requests.

2. GitHub MCP Implementation

GitHub MCP connects AI tools directly to GitHub repositories and workflows. It uses GitHub Actions to run MCP tools when triggered by repository events.

This allowed us to automate code reviews and tool invocations without leaving the GitHub ecosystem. The integration felt natural for our developers who were already using GitHub daily.

- name: Run MCP Server
  uses: actions/setup-python@v2
  with:
    script

Key features:

• Native integration with GitHub repositories

• Supports GitHub Actions for automation

• Access to repository metadata and content

The main limitation is that it only works within the GitHub ecosystem.

3. Cursor MCP

Cursor MCP is a lightweight, local-first MCP server designed for rapid development and testing. It runs directly on a developer's machine.

This reduced our iteration time dramatically when testing tool behavior changes. We could make adjustments and see results immediately without deployment cycles.

Key features:

• Local development environment

• Minimal dependencies

• Support for hot-reloading changes

The main limitation is that it's not designed for production use at scale.

4. Postgres MCP

Postgres MCP allows AI models to interact directly with PostgreSQL databases using SQL. It translates natural language requests into database queries.

This enabled easier querying of production data for analytics tasks. Our data team could use AI to explore datasets without writing complex SQL by hand.

SELECT * FROM users WHERE active = true

Key features:

• Direct SQL query execution

• Automatic schema discovery

• Secure database access controls

The main limitation is that it only works with PostgreSQL databases and requires careful access control configuration.

5. Browsertools MCP

Browsertools MCP connects AI models to browser automation tools like Puppeteer. It allows models to control web browsers programmatically.

This made it possible to extract and process data from dynamic websites that require JavaScript execution. We automated several web-based workflows that were previously manual.

Key features:

• Headless browser automation

• Support for multiple browser engines

• Script-based interaction patterns

The main limitation is that browser automation is resource-intensive and requires managing browser versions.

6. IDE Integrated MCP

IDE Integrated MCP embeds MCP functionality directly into code editors like VSCode and JetBrains products. It allows tools to be accessed without switching contexts.

This reduced context switching and streamlined our development workflow. Developers could invoke AI tools without leaving their editor.

Key features:

• In-editor command execution

• Real-time feedback in the editor

• Customizable tool palette

The main limitation is that plugin support varies across different editors, and updates must be tested with each IDE version.

7. Auto Generated Docs MCP

Auto Generated Docs MCP creates and serves up-to-date API documentation through an MCP interface. It converts OpenAPI specs into interactive documentation.

This eliminated the need to manually update docs after schema changes. Our documentation stayed in sync with the actual API implementation automatically.

Key features:

• Live documentation updates

• Interactive API exploration

• Version tracking for schemas

The main limitation is that it focuses on documentation rather than full API integration.

8. Sequential Thinking MCP

Sequential Thinking MCP helps break complex tasks into smaller, logical steps. It structures AI reasoning into distinct phases with reflection between them.

This improved the quality of AI-generated solutions for complex problems. We saw better results on architectural design tasks and system debugging.

Key features:

• Step-by-step reasoning framework

• Reflection capabilities between steps

• Support for complex problem decomposition

The main limitation is that it adds overhead for simpler tasks that don't require structured thinking.

9. Memory Bank MCP

Memory Bank MCP provides persistent storage for AI conversations and context. It allows information to be recalled across multiple sessions.

This gave our AI tools continuity across different interactions. They could remember previous decisions and context without starting from scratch each time.

Key features:

• Cross-session memory persistence

• Contextual information retrieval

• Knowledge graph-based storage

The main limitation is that memory management requires careful curation to avoid context pollution.

10. File System MCP

File System MCP provides secure access to local files and directories. It allows AI models to read, write, and manage files within defined boundaries.

This enabled our AI tools to work directly with local development artifacts. Code generation and file manipulation became much more practical.

Key features:

• Secure file system access

• Directory traversal and search

• File content manipulation

The main limitation is that security boundaries must be carefully configured to prevent unauthorized access.

Key Benefits Of MCP In Large Scale Integrations

MCP servers have transformed how we handle API integrations at scale. They provide a standardized way to describe and access tools, which reduces the complexity of connecting different systems.

The most significant benefit is the reduction in integration time. What used to take weeks now takes days, and onboarding new developers to an API is much faster.

Before implementing awesome MCP servers, we faced common challenges with API integrations:

• Schema inconsistencies between documentation and implementation

• Manual endpoint mapping for each new integration

• Difficulty maintaining up-to-date documentation

• Slow developer onboarding due to complex setup processes

MCP servers address these issues by providing a consistent interface for AI systems to work with APIs. The standardized format means that once developers understand MCP, they can work with any MCP-compatible tool.

Tips For Seamless Endpoint Management

1. Consolidate Schemas Early

OpenAPI specs typically separate input definitions across parameters and request bodies. When converting to an MCP Tool, combine these into a single schema to avoid conflicts.

Before:

parameters:
  - name: userId
    in: path
  - name: filter
    in

After:

schema:
  type: object
  properties:
    userId:
      type: string
    filter:
      type

This consolidation makes the schema more consistent and easier to validate.

2. Resolve External References

OpenAPI specs often use $ref to point to shared components. MCP Tool schemas need to be self-contained, so these references must be resolved.

This involves replacing each $ref with the actual content it points to. Be careful with recursive references to avoid infinite loops.

3. Use Dynamic Loading For Large APIs

Many APIs have hundreds of endpoints, which can be too many to load at once. Implement dynamic loading to bring in tools only when needed.

This approach reduces memory usage and startup time while still providing access to the full API when required.

Best MCP Servers

We expect the list of top MCP servers to change quickly, some of the best MCP servers include features like schema validation, tool discovery, and multiple transport options.

The ecosystem is still evolving, but the core benefits of standardization and interoperability are already clear. We

At Stainless, we've found that MCP servers work particularly well with our SDK generation process. The structured schemas and standardized interfaces align perfectly with our approach to creating idiomatic client libraries.

Get started with Stainless to see how our SDK generation can complement your MCP server implementation.

FAQs About MCP Servers

What are the most common challenges when implementing MCP servers?

Schema management and reference resolution are the biggest challenges, especially with complex OpenAPI specs. Converting nested references and handling circular dependencies requires careful planning and sometimes custom tooling.

How do MCP servers improve developer productivity?

MCP servers standardize tool interfaces and automate validation, reducing the manual code needed for integrations. Developers can focus on tool behavior rather than connection details, which speeds up development cycles.

Can MCP servers work with existing OpenAPI specifications?

Yes, most MCP servers can convert OpenAPI specs into MCP-compatible tool definitions. The conversion process typically involves merging parameters, resolving references, and adapting the schema format.

What's the difference between awesome MCP servers and basic implementations?

Awesome MCP servers include features like schema validation, tool discovery, and multiple transport options. Basic implementations might only support a single tool with minimal validation or standardization.

How do you measure the success of an MCP server implementation?

Success metrics include integration time reduction, decreased error rates, faster developer onboarding, and reduced maintenance overhead. Comparing these metrics before and after implementation provides a clear picture of impact.