Developing an MCP server involves working with invisible data streams. Your Python or TypeScript code communicates over standard input and output (stdio), exchanging JSON-RPC messages that are difficult to track without interception. Before attempting to connect your server to an LLM client like Claude Desktop, you must verify that your implementation adheres to the protocol and behaves as expected. The MCP Inspector serves as this verification layer.
The MCP Inspector is a developer tool that runs as a web interface. It acts as a surrogate client, connecting to your server and allowing you to browse resources, execute tools, and adjust prompts manually. This isolation removes the variability of the LLM from the equation. If a tool fails within the Inspector, the issue lies within your code. If it succeeds in the Inspector but fails with the LLM, the issue likely lies in the tool description or schema clarity.
The Inspector is distributed as a Node.js package. You do not need to install it globally; it is best executed using npx to ensure you are using the latest version of the debugging protocol. The command structure requires you to pass the command you normally use to run your server as an argument to the Inspector.
For a Python server using uv, the command follows this pattern:
npx @modelcontextprotocol/inspector uv run server.py
For a TypeScript server built or running directly:
npx @modelcontextprotocol/inspector node build/index.js
Upon execution, the Inspector spawns your server as a child process. It attaches to the server's stdin and stdout streams, acting as the transport layer. Simultaneously, it starts a local web server (typically on localhost:5173) where you can interact with the UI.
Architecture of the debugging session showing the separation between the UI, the Inspector proxy, and the target server.
Once the web interface loads, the Inspector initiates the initialize handshake. If successful, the "Resources" tab populates with the list of data endpoints your server exposes. This view verifies two critical aspects of your implementation. First, it confirms that the resources/list handler is returning the correct metadata structure. Second, it allows you to test the content retrieval logic.
Clicking on a specific resource sends a resources/read request with the specific URI. The Inspector renders the returned content (text or binary) in the main view. This is particularly useful for debugging dynamic resources. For example, if your resource URI includes a parameter like file:///logs/{date}, the Inspector allows you to verify that your pattern matching logic correctly routes the request and extracts the variable.
Tools often contain the most complex logic in an MCP server. The Inspector generates a dynamic form for every tool defined in your schema. This form is derived strictly from the inputSchema JSON Schema you provided in the code.
If a required field is missing from the form, or if a field allows a string when it should be an integer, this indicates an error in your Pydantic model or Zod definition. You can input test values into these forms and execute the tool. The Inspector sends the tools/call request and displays the result.
This manual execution is necessary to catch logic errors that do not trigger exceptions. For instance, a database query tool might technically succeed (return a 200 OK status) but return an empty list due to a malformed SQL statement. By inspecting the raw JSON output in the Inspector, you can distinguish between a "no data found" scenario and a silent failure.
Typical breakdown of request types observed during a server debugging session.
The most powerful feature of the Inspector is the connection log. Located in the bottom panel or a dedicated tab, this log records every JSON-RPC message exchanged between the Inspector and your server.
When debugging, you are looking for three specific components in the log flow:
tools/call) and the params object. Ensure arguments are nested correctly.id (integer or string). The response from your server must carry the same id. If the IDs do not match, the client will discard the response.Consider the standard error format:
If your server prints a Python stack trace directly to stdout, it violates the JSON structure, and the Inspector will report a parsing error. The log allows you to pinpoint exactly where the server output deviated from the JSON specification.
A frequent issue during integration is "stdio noise." This occurs when your server code or a dependency prints debug information (like print("Connecting to DB...")) to standard output. Because MCP uses standard output for protocol communication, these plain text strings corrupt the JSON stream.
The Inspector separates stdout (protocol) and stderr (logs). To fix parsing errors seen in the Inspector, ensure all your application logging is directed to stderr. In Python, use the logging module configured to write to sys.stderr. In Node.js, use console.error() for logs and keep console.log() reserved strictly for MCP messages if you are managing the transport manually (though using the SDK abstraction is recommended).
The Inspector runs in its own process context. It does not automatically inherit specific environment variables (like API keys) unless they are present in your current shell session. If your server fails to authenticate with external services while running under the Inspector, checking the environment configuration is the first step.
You can pass environment variables inline when starting the Inspector:
# Linux/macOS
OPENAI_API_KEY=sk-... npx @modelcontextprotocol/inspector uv run server.py
# Windows PowerShell
$env:OPENAI_API_KEY="sk-..."; npx @modelcontextprotocol/inspector uv run server.py
By systematically validating resources, tools, and message integrity using the Inspector, you ensure that the server is strong enough to handle the unpredictable nature of LLM-driven interactions in the subsequent integration phase.
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logging - Logging facility for Python | Python Documentation, Python Software Foundation, 2024 (Python Software Foundation) - Official documentation for Python's logging module, providing guidance on configuring logs, including directing output to standard error to avoid corrupting standard output streams.© 2026 ApX Machine LearningEngineered with