MCP Interviewer
Catch MCP server issues before your agents do.
What is MCP Interviewer?
MCP Interviewer is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to catch mcp server issues before your agents do.
Catch MCP server issues before your agents do.
This server falls under the Developer Tools category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Catch MCP server issues before your agents do.
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-interviewerConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCP Interviewer
MCP Interviewer is a Microsoft-developed Python CLI tool that acts as a quality-assurance layer for MCP servers — it connects to any MCP server, inspects its declared tools, validates them against provider constraints (such as OpenAI's 128-tool limit), optionally runs functional tests by driving the server with an LLM agent, and produces structured Markdown/JSON reports. It is designed to catch schema issues, missing descriptions, constraint violations, and runtime failures before your AI agents encounter them in production. Developers and teams building or integrating MCP servers use it as part of their CI pipeline or pre-release checklist.
Prerequisites
- Python 3.9 or later installed
- pip or uv available for package installation
- An OpenAI API key (or compatible LLM) if you enable functional testing (--test) or LLM evaluation (--judge)
- Docker recommended for running untrusted MCP servers in isolation during security-sensitive testing
Install mcp-interviewer via pip
Install the package from PyPI using pip or uv.
pip install mcp-interviewer
# or with uv:
uv add mcp-interviewerRun a basic constraint check against an MCP server
Pass the full command to launch your target MCP server as the argument to mcp-interviewer. The tool starts the server, inspects its tools, and reports constraint violations.
mcp-interviewer "npx -y @modelcontextprotocol/server-everything"Enable functional testing with an LLM agent
Add --test and --model to run the server's tools end-to-end via an LLM agent. This exercises real tool calls and surfaces runtime failures.
mcp-interviewer --test --model gpt-4.1 \
"docker run -i --rm node:lts npx -y @modelcontextprotocol/server-everything"Generate structured reports
Use --reports to select which sections to include in the output, and optionally add --judge for LLM-based quality assessments.
mcp-interviewer --test --judge --model gpt-4.1 \
--reports summary constraints tools test \
"npx -y @modelcontextprotocol/server-filesystem /tmp"Customise constraint checks for your target provider
Use --constraints to specify which constraint sets to apply. Different LLM providers have different tool limits and schema requirements.
mcp-interviewer --constraints openai anthropic \
"npx -y @modelcontextprotocol/server-github"MCP Interviewer Examples
Checking an MCP server before agent use
Typical CI-style invocation that validates a filesystem MCP server against OpenAI constraints and produces a report.
# Install
pip install mcp-interviewer
# Run validation
mcp-interviewer --constraints openai \
--reports summary constraints tools \
"npx -y @modelcontextprotocol/server-filesystem /workspace"Prompts to try
Use-case scenarios you can validate with mcp-interviewer — these are the kinds of questions it answers about your server.
- "Does my MCP server exceed OpenAI's 128-tool limit?"
- "Are all tools missing required description fields?"
- "Run the filesystem server tools and confirm list_directory returns valid JSON"
- "Evaluate tool naming conventions and parameter schemas for LLM compatibility"
- "Generate a full audit report of the GitHub MCP server before deploying it to agents"Troubleshooting MCP Interviewer
Functional testing (--test) fails with an authentication error
The --test flag requires a valid LLM API key. Set the OPENAI_API_KEY environment variable before running, or pass a custom --client implementation for other providers.
The MCP server process exits immediately with no output
mcp-interviewer executes the provided command as a child process. Ensure the command works standalone in your shell first. For untrusted servers, wrap the command in a Docker container: docker run -i --rm node:lts npx -y <your-server>
Reports contain no tool information
The target server must implement the MCP tools/list method correctly. If your server only exposes resources or prompts, those sections will be empty. Check that your server responds to the MCP initialize handshake by testing the command manually.
Frequently Asked Questions about MCP Interviewer
What is MCP Interviewer?
MCP Interviewer is a Model Context Protocol (MCP) server that catch mcp server issues before your agents do. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCP Interviewer?
Follow the installation instructions on the MCP Interviewer GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCP Interviewer?
MCP Interviewer works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCP Interviewer free to use?
Yes, MCP Interviewer is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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