This server exposes only one tool (parse_sentence) with a minimal, non-idiomatic interface. The tool name lacks a clear action verb prefix (should be 'parse_sentence', which it is, but the description is vague about intent and scope). The input schema is present but extremely sparse, only a single string parameter with no constraints, range limits, or format guidance. The description is short (79 chars) but lacks critical context about prerequisites, error conditions, output structure, and when to use this tool vs. alternatives. No output schema is documented anywhere in the code; the function returns a complex nested structure (tokens[0] with a 'tree' key containing recursive token objects) that an LLM must infer from code inspection. The server performs READ_ONLY operations but has no READ_ONLY or idempotent tool annotations. Error handling is minimal, a ValueError is raised for newlines but never caught or formatted for the LLM; the Flask route has no try/catch. The implementation uses a custom HTTP transport (Flask) not aligned with MCP's Streamable HTTP standard, and the server is not a proper MCP server at all, it's a raw Flask app that does not implement the MCP protocol.

• Implement this as a proper MCP server using the MCP Python SDK (https://github.com/modelcontextprotocol/python-sdk) rather than a raw Flask app. MCP provides the protocol layer, transport, and client negotiation out of the box.
• Add a detailed output schema documenting the structure returned: { sentence: string, tree: { relation_name: [ { index: int, token: string, pos: string, label: string, ... }, ... ] } }. Include descriptions of what each field means (e.g., 'pos' = part-of-speech tag, 'relation' = syntactic dependency type).
• Expand the tool description to 100 - 150 chars explaining: (1) what it does (POS + dependency parsing), (2) when to call it (whenever you need syntactic analysis), (3) what the output means (a tree of dependencies), (4) limitations (no newlines, requires well-formed English sentences).
• Add parameter constraints to the 'sentence' input: max length (e.g., 500 chars to avoid timeouts), explicit rejection of newlines with guidance ('Use multiple calls for multi-line text'), and a note that single sentences work best.
• Wrap the subprocess call with try/except, catching TimeoutError, CalledProcessError, and OSError. Return structured error responses: { error: 'timeout', message: 'Parsing took too long; try a shorter sentence', retryable: true } instead of raw 500 errors.
• Add readOnlyHint: true and idempotentHint: true to the tool definition so LLMs know this is safe to call repeatedly without side effects.
• Set an explicit timeout on the Popen subprocess (e.g., 10 seconds) and catch timeout to return a user-friendly error with suggestions (e.g., 'Sentence too complex; try breaking it into shorter chunks').