The server has 4 tools with basic STDIO-based MCP registration using fastmcp. All tools are properly registered with @mcp.tool() decorators and have docstring descriptions. However, critical quality issues emerge: (1) Descriptions are minimal (13-70 chars), falling below the 50-200 char baseline for LLM-optimized documentation. (2) Input schemas are present in the decorator signatures but lack explicit type definitions and parameter descriptions required by the rubric. (3) Error handling is generic exception catching with minimal guidance for LLM recovery. (4) No output schema documentation. (5) No validation or parameter constraints. The tools themselves follow reasonable naming conventions (summarize_video, get_summary, create_chat, get_chat) with clear verb_noun structure, but descriptions lack the WHAT/WHEN/WHY context needed for LLM tool selection.
Tools (4)
create_chatwritesource verified
Creates a new chat session via /chat endpoint.
get_chatread onlysource verified
Retrieves chat details from /chat/<chat_id>.
get_summaryread onlysource verified
Retrieves video summary details by calling /summarize/<summary_id>.
summarize_videowritesource verified
Initiates video summarization by calling the /summarize endpoint.
Descriptions are 28-70 characters, well below the 50-200 char baseline for LLM-optimized documentation. Every tool description lacks explicit WHAT/WHEN/WHY guidance, making it harder for LLMs to select the right tool and understand preconditions.
No parameter descriptions in input schemas. The rubric requires every parameter to have a description explaining what it controls. LLMs cannot infer meaning from parameter names alone (e.g., what format is video_id? Is it YouTube video ID or internal system ID?).
No output schema documentation. Tools return response.json() from an external API, but the server does not document what fields the LLM should expect. This forces LLMs to infer structure from trial and error, increasing hallucination risk.
summarize_videoget_summary
Recommendations
Expand all tool descriptions to 50-200 characters. Include WHAT the tool does, WHEN to use it (e.g., 'Call summarize_video first to initiate a summary job'), and WHAT it returns. Example: 'Initiates video summarization asynchronously. Returns a summary_id that you can poll with get_summary(). Call this first if you only have a video URL or ID.'
Add explicit descriptions to every input parameter in the JSON schema. Use the format: parameter_name (type, constraints): description. Example: 'video_id (string, required): YouTube video ID or internal video reference. Format: 11-character alphanumeric (e.g., dQw4w9WgXcQ) or internal UUID.'
Document the output schema for each tool. Specify what fields the response contains, their types, and examples. Example: 'Returns {summary_id (string), status (string: queued|processing|completed|failed), created_at (ISO 8601 string), video_title (string), summary_text (string)}.'
Replace generic exception handling with structured error responses. Categorize errors (retryable: network timeout, 429; user-fixable: invalid video_id format; fatal: service unavailable). Return: {'error': '<error_code>', 'message': '<user-facing message>', 'recovery': '<suggested next step>'} Example: {'error': 'INVALID_VIDEO_ID', 'message': 'video_id must be 11-character alphanumeric', 'recovery': 'Extract the video ID from the YouTube URL (e.g., youtube.com/watch?v=<VIDEO_ID>) and retry.'}'
Fix mutable default argument in create_chat. Change `chat_history: list = []` to `chat_history: list = None` and add `if chat_history is None: chat_history = []` in the function body.
Generic exception handling returns {'status': 'error', 'message': str(e)}. Error messages are raw exception strings (network timeouts, JSON decode errors) that give LLMs no actionable recovery guidance. Per the rubric, errors must be categorized (retryable vs fatal) and must suggest next steps.
create_chat uses mutable default argument (chat_history: list = []). This is a Python anti-pattern that can cause subtle state-sharing bugs across invocations.
No input validation or constraints. Parameters accept any string value with no format validation, enum constraints, or range checks. An LLM can pass invalid video IDs, malformed summary IDs, or oversized message strings without client-side rejection.
No pagination support. If the API returns large lists (e.g., chat_history with many messages), the server does not offer limit/offset parameters or pagination hints. This risks context window overflow.
Tool descriptions do not clarify dependencies or workflows. For example, summarize_video and get_summary suggest you must call them in sequence, but the description does not state this. Same with create_chat and get_chat. LLMs will struggle with sequencing without explicit dependency hints.
summarize_videoget_summarycreate_chatget_chat
Add input validation. Use JSON Schema enum constraints, minLength, maxLength, pattern for parameters. Example: 'video_id: {type: string, pattern: ^[a-zA-Z0-9_-]{11}$, description: YouTube video ID}'.
Add pagination hints. If get_chat or any tool can return large lists, add optional limit (1-100, default=20) and offset (0-based) parameters. Document max result size in the tool description.
Clarify tool sequencing in descriptions. Example in summarize_video: 'Use this to initiate summarization. The response contains a summary_id, pass this to get_summary() to poll for the result.' Make the required workflow explicit.
Add tool composition hints. After get_summary or create_chat returns a summary_id or chat_id, the description should note: 'Use the summary_id with get_summary() or create_chat() for follow-up actions.'
Consider wrapping the external API's response. If the API returns verbose metadata, timestamps, or internal IDs irrelevant to chat, strip them server-side before returning to the LLM. Return only user-facing fields (summary text, status, chat message). This saves tokens and reduces hallucination risk.