developer-tools

OpenAPI

matthewhand

by matthewhand

OpenAPI enables seamless integration of external services via REST APIs like Jira and Confluence, using OpenAPI specs fo

Dynamically exposes REST APIs defined by OpenAPI specifications as MCP tools, enabling seamless integration of external services into workflows.

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    what it does

    Dynamically exposes REST APIs defined by OpenAPI specifications as MCP tools, enabling seamless integration of external services into workflows.

    about

    OpenAPI is a community-built MCP server published by matthewhand that provides AI assistants with tools and capabilities via the Model Context Protocol. OpenAPI enables seamless integration of external services via REST APIs like Jira and Confluence, using OpenAPI specs fo It is categorized under developer tools.

    how to install

    You can install OpenAPI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

    license

    MIT

    OpenAPI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

    readme

    mcp-openapi-proxy

    mcp-openapi-proxy is a Python package that implements a Model Context Protocol (MCP) server, designed to dynamically expose REST APIs—defined by OpenAPI specifications—as MCP tools. This facilitates seamless integration of OpenAPI-described APIs into MCP-based workflows.

    Table of Contents

    Overview

    The package offers two operational modes:

    • Low-Level Mode (Default): Dynamically registers tools corresponding to all valid API endpoints specified in an OpenAPI document (e.g. /chat/completions becomes chat_completions()).
    • FastMCP Mode (Simple Mode): Provides a streamlined approach by exposing a predefined set of tools (e.g. list_functions() and call_function()) based on static configurations.

    Features

    • Dynamic Tool Generation: Automatically creates MCP tools from OpenAPI endpoint definitions.
    • Simple Mode Option: Offers a static configuration alternative via FastMCP mode.
    • OpenAPI Specification Support: Compatible with OpenAPI v3 with potential support for v2.
    • Flexible Filtering: Allows endpoint filtering through whitelisting by paths or other criteria.
    • Payload Authentication: Supports custom authentication via JMESPath expressions (e.g. for APIs like Slack that expect tokens in the payload not the HTTP header).
    • Header Authentication: Uses Bearer by default for API_KEY in the Authorization header, customizable for APIs like Fly.io requiring Api-Key.
    • MCP Integration: Seamlessly integrates with MCP ecosystems for invoking REST APIs as tools.

    Installation

    Install the package directly from PyPI using the following command:

    uvx mcp-openapi-proxy
    

    MCP Ecosystem Integration

    To incorporate mcp-openapi-proxy into your MCP ecosystem configure it within your mcpServers settings. Below is a generic example:

    {
        "mcpServers": {
            "mcp-openapi-proxy": {
                "command": "uvx",
                "args": ["mcp-openapi-proxy"],
                "env": {
                    "OPENAPI_SPEC_URL": "${OPENAPI_SPEC_URL}",
                    "API_KEY": "${API_OPENAPI_KEY}"
                }
            }
        }
    }
    

    Refer to the Examples section below for practical configurations tailored to specific APIs.

    Modes of Operation

    FastMCP Mode (Simple Mode)

    • Enabled by: Setting the environment variable OPENAPI_SIMPLE_MODE=true.
    • Description: Exposes a fixed set of tools derived from specific OpenAPI endpoints as defined in the code.
    • Configuration: Relies on environment variables to specify tool behavior.

    Low-Level Mode (Default)

    • Description: Automatically registers all valid API endpoints from the provided OpenAPI specification as individual tools.
    • Tool Naming: Derives tool names from normalized OpenAPI paths and methods.
    • Behavior: Generates tool descriptions from OpenAPI operation summaries and descriptions.

    Environment Variables

    • OPENAPI_SPEC_URL: (Required) The URL to the OpenAPI specification JSON file (e.g. https://example.com/spec.json or file:///path/to/local/spec.json).
    • OPENAPI_LOGFILE_PATH: (Optional) Specifies the log file path.
    • OPENAPI_SIMPLE_MODE: (Optional) Set to true to enable FastMCP mode.
    • TOOL_WHITELIST: (Optional) A comma-separated list of endpoint paths to expose as tools.
    • TOOL_NAME_PREFIX: (Optional) A prefix to prepend to all tool names.
    • API_KEY: (Optional) Authentication token for the API sent as Bearer <API_KEY> in the Authorization header by default.
    • API_AUTH_TYPE: (Optional) Overrides the default Bearer Authorization header type (e.g. Api-Key for GetZep).
    • STRIP_PARAM: (Optional) JMESPath expression to strip unwanted parameters (e.g. token for Slack).
    • DEBUG: (Optional) Enables verbose debug logging when set to "true", "1", or "yes".
    • EXTRA_HEADERS: (Optional) Additional HTTP headers in "Header: Value" format (one per line) to attach to outgoing API requests.
    • SERVER_URL_OVERRIDE: (Optional) Overrides the base URL from the OpenAPI specification when set, useful for custom deployments.
    • TOOL_NAME_MAX_LENGTH: (Optional) Truncates tool names to a max length.
    • Additional Variable: OPENAPI_SPEC_URL_<hash> – a variant for unique per-test configurations (falls back to OPENAPI_SPEC_URL).
    • IGNORE_SSL_SPEC: (Optional) Set to true to disable SSL certificate verification when fetching the OpenAPI spec.
    • IGNORE_SSL_TOOLS: (Optional) Set to true to disable SSL certificate verification for API requests made by tools.

    Examples

    For testing you can run the uvx command as demonstrated in the examples then interact with the MCP server via JSON-RPC messages to list tools and resources. See the "JSON-RPC Testing" section below.

    Glama Example

    image

    Glama offers the most minimal configuration for mcp-openapi-proxy requiring only the OPENAPI_SPEC_URL environment variable. This simplicity makes it ideal for quick testing.

    1. Verify the OpenAPI Specification

    Retrieve the Glama OpenAPI specification:

    curl https://glama.ai/api/mcp/openapi.json
    

    Ensure the response is a valid OpenAPI JSON document.

    2. Configure mcp-openapi-proxy for Glama

    Add the following configuration to your MCP ecosystem settings:

    {
        "mcpServers": {
            "glama": {
                "command": "uvx",
                "args": ["mcp-openapi-proxy"],
                "env": {
                    "OPENAPI_SPEC_URL": "https://glama.ai/api/mcp/openapi.json"
                }
            }
        }
    }
    

    3. Testing

    Start the service with:

    OPENAPI_SPEC_URL="https://glama.ai/api/mcp/openapi.json" uvx mcp-openapi-proxy
    

    Then refer to the JSON-RPC Testing section for instructions on listing resources and tools.

    Fly.io Example

    image

    Fly.io provides a simple API for managing machines making it an ideal starting point. Obtain an API token from Fly.io documentation.

    1. Verify the OpenAPI Specification

    Retrieve the Fly.io OpenAPI specification:

    curl https://raw.githubusercontent.com/abhiaagarwal/peristera/refs/heads/main/fly-machines-gen/fixed_spec.json
    

    Ensure the response is a valid OpenAPI JSON document.

    2. Configure mcp-openapi-proxy for Fly.io

    Update your MCP ecosystem configuration:

    {
        "mcpServers": {
            "flyio": {
                "command": "uvx",
                "args": ["mcp-openapi-proxy"],
                "env": {
                    "OPENAPI_SPEC_URL": "https://raw.githubusercontent.com/abhiaagarwal/peristera/refs/heads/main/fly-machines-gen/fixed_spec.json",
                    "API_KEY": "<your_flyio_token_here>"
                }
            }
        }
    }
    
    • OPENAPI_SPEC_URL: Points to the Fly.io OpenAPI specification.
    • API_KEY: Your Fly.io API token (replace <your_flyio_token_here>).
    • API_AUTH_TYPE: Set to Api-Key for Fly.io’s header-based authentication (overrides default Bearer).

    3. Testing

    After starting the service refer to the JSON-RPC Testing section for instructions on listing resources and tools.

    Render Example

    image

    Render offers infrastructure hosting that can be managed via an API. The provided configuration file examples/render-claude_desktop_config.json demonstrates how to set up your MCP ecosystem quickly with minimal settings.

    1. Verify the OpenAPI Specification

    Retrieve the Render OpenAPI specification:

    curl https://api-docs.render.com/openapi/6140fb3daeae351056086186
    

    Ensure the response is a valid OpenAPI document.

    2. Configure mcp-openapi-proxy for Render

    Add the following configuration to your MCP ecosystem settings:

    {
        "mcpServers": {
            "render": {
                "command": "uvx",
                "args": ["mcp-openapi-proxy"],
                "env": {
                    "OPENAPI_SPEC_URL": "https://api-docs.render.com/openapi/6140fb3daeae351056086186",
                    "TOOL_WHITELIST": "/services,/maintenance",
                    "API_KEY": "your_render_token_here"
                }
            }
        }
    }
    

    3. Testing

    Launch the proxy with your Render configuration:

    OPENAPI_SPEC_URL="https://api-docs.render.com/openapi/6140fb3daeae351056086186" TOOL_WHITELIST="/services,/maintenance" API_KEY="your_render_token_here" uvx mcp-openapi-proxy
    

    Then refer to the JSON-RPC Testing section for instructions on listing resources and tools.

    Slack Example

    image

    Slack’s API showcases stripping unnecessary token payload using JMESPath. Obtain a bot token from Slack API documentation.

    1. Verify the OpenAPI Specification

    Retrieve the Slack OpenAPI specification:

    
    ---
    

    FAQ

    What is the OpenAPI MCP server?
    OpenAPI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
    How do MCP servers relate to agent skills?
    Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
    How are reviews shown for OpenAPI?
    This profile displays 44 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

    Use Cases

    Extended AI Capabilities

    Add new capabilities to Claude beyond text generation

    Example

    Access external data sources, execute code, interact with tools and services

    Transform Claude from chatbot to action-taking agent

    Context Enhancement

    Provide Claude with access to relevant context and data

    Example

    Load project documentation, access knowledge bases, query databases

    Get more accurate, context-aware responses

    Workflow Automation

    Automate multi-step workflows combining AI and external tools

    Example

    Research → Summarize → Create document → Send notification

    Complete complex tasks end-to-end without manual steps

    Implementation Guide

    Prerequisites

    • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
    • Basic understanding of MCP architecture and capabilities
    • Access credentials for integrated services (if required)
    • Willingness to experiment and iterate on configuration

    Time Estimate

    15-60 minutes depending on server complexity

    Installation Steps

    1. 1.Install MCP server: npm install -g [package-name] or via GitHub
    2. 2.Add server configuration to ~/.claude/mcp.json
    3. 3.Provide required credentials and configuration
    4. 4.Restart Claude Desktop to load new server
    5. 5.Test basic functionality with simple prompts
    6. 6.Explore capabilities and experiment with use cases
    7. 7.Document successful patterns for reuse

    Troubleshooting

    • MCP server not loading: Check config syntax, verify installation
    • Connection errors: Check network, firewall, credentials
    • Feature not working: Read server docs, check required parameters
    • Performance issues: Monitor resource usage, check for network latency
    • Conflicts with other servers: Check port assignments, namespace collisions

    Best Practices

    ✓ Do

    • +Read server documentation thoroughly before setup
    • +Start with simple use cases to validate functionality
    • +Test in non-production environment first
    • +Monitor resource usage and performance
    • +Keep servers updated for bug fixes and new features
    • +Document configuration for team members
    • +Use environment variables for sensitive configuration

    ✗ Don't

    • Don't grant overly permissive access to MCP servers
    • Don't skip reading security considerations in docs
    • Don't expose sensitive data without proper controls
    • Don't run untrusted MCP servers without code review
    • Don't ignore error messages—investigate root cause

    💡 Pro Tips

    • Combine multiple MCP servers for powerful workflows
    • Create custom MCP servers for your specific needs
    • Share successful configurations with team
    • Use MCP inspector for debugging
    • Join MCP community for tips and troubleshooting

    Technical Details

    Architecture

    Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

    Protocols

    • Model Context Protocol (MCP)
    • JSON-RPC 2.0
    • stdio or HTTP transport

    Compatibility

    • Claude Desktop
    • Cursor IDE
    • Custom MCP clients

    When to Use This

    ✓ Use When

    Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

    ✗ Avoid When

    Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

    Integration

    • Tool composition: Chain multiple MCP tools in workflows
    • Context augmentation: Provide AI with relevant external data
    • Action delegation: Let AI execute tasks on external systems
    • Bidirectional sync: Keep AI context and external systems in sync

    Discussion

    Product Hunt–style comments (not star reviews)
    • No comments yet — start the thread.

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    Ratings

    4.644 reviews
    • Rahul Santra· Dec 24, 2024

      OpenAPI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

    • Omar Choi· Dec 20, 2024

      Strong directory entry: OpenAPI surfaces stars and publisher context so we could sanity-check maintenance before adopting.

    • Anaya Sethi· Dec 12, 2024

      I recommend OpenAPI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

    • Pratham Ware· Nov 15, 2024

      OpenAPI is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

    • Anika Flores· Nov 11, 2024

      OpenAPI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

    • Arya Sharma· Nov 7, 2024

      Useful MCP listing: OpenAPI is the kind of server we cite when onboarding engineers to host + tool permissions.

    • Xiao Rao· Nov 3, 2024

      According to our notes, OpenAPI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

    • Arjun Abebe· Oct 26, 2024

      OpenAPI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

    • Xiao Flores· Oct 22, 2024

      OpenAPI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

    • Sakshi Patil· Oct 6, 2024

      We evaluated OpenAPI against two servers with overlapping tools; this profile had the clearer scope statement.

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