Static source inference · medium confidence · detected: Logging
Deprecated protocol patterns detected
Summary
Single tool with minimal quality signals. The tool 'analyze_youtube' has a basic description and input schema, but lacks essential quality markers for production use. The description is 79 characters, within acceptable range but generic. Parameters have types and brief descriptions, but lack format constraints, valid ranges, or actionable error guidance. No output schema is documented. The tool does not follow verb_noun naming convention strictly (starts with 'analyze' which is appropriate, but the composite name obscures the action). No error handling guidance for common failure modes (invalid URL, quota exceeded, network timeout). Security concern: API key is injected via environment variable (good), but the tool provides no rate-limiting, timeout specifications, or recovery instructions. Composition is single-purpose (appropriate), but lacks pagination, result limits, or structured output specification.
Tools (1)
analyze_youtuberead onlyauthsource verified60/100
Analyze a YouTube video's transcript and visual content using Gemini API.
No documented output schema. Tool returns dict with 'response' or 'error' fields, but LLM has no formal specification of the structure, types, or fields to expect. This forces the LLM to infer the response shape from trial and error.
Parameter descriptions lack actionable constraints. 'youtube_url' description does not specify format (must be valid YouTube URL, not arbitrary string). 'prompt' lacks guidance on length, content type, or examples. LLM may pass invalid YouTube URLs or excessively long prompts without validation hints.
Error handling provides no recovery guidance. Tool returns generic error dicts ('error' field with plain text). No categorization of error type (retryable, user-fixable, fatal). LLM cannot distinguish 'API key not configured' (fatal, needs user action) from 'network timeout' (retryable) or 'invalid YouTube URL' (user-fixable).
analyze_youtube
Recommendations
Add a formal output schema to the tool docstring: specify that the response is a dict with 'response' (string) or 'error' (string, with optional 'error_code' and 'retry_hint' fields). Example: {'response': '<analysis text>', 'error': '<message>', 'error_code': 'INVALID_URL|QUOTA_EXCEEDED|TIMEOUT|AUTH_FAILED', 'retry_hint': '<recovery instruction>'}
Enhance parameter descriptions with format constraints. For 'youtube_url': 'A valid YouTube URL (https://www.youtube.com/watch?v=..., https://youtu.be/..., or youtube.com/shorts/...). Must be publicly accessible or owned by the authenticated user.' For 'prompt': 'The analysis question or instruction (max 1000 chars). Examples: "Summarize the key points", "Extract technical details", "Identify speakers".'
Implement error categorization and recovery guidance in the response. Return: {'error': '<message>', 'error_code': 'INVALID_URL', 'category': 'user_fixable', 'recovery': 'Check the YouTube URL format and ensure the video is publicly accessible or owned by you.'} for invalid URLs; 'QUOTA_EXCEEDED' with category 'retryable' and recovery 'Wait 60 seconds and retry'; 'TIMEOUT' with category 'retryable' and recovery 'The analysis took too long. Try a shorter video or simpler prompt.'
Add timeout specification: Set an explicit timeout (e.g., 120 seconds) for Gemini API calls. Document this in the tool description: 'Analysis timeout: 120 seconds. Long or complex videos may exceed this limit.'
Add result size limits and truncation hints: Cap the response text to 4000 chars; if truncated, return {'response': '<truncated text>...', 'truncated': true, 'full_length': <original length>} so the LLM knows more data exists.
Spec posture evidence
Inferred effective spec: <=2025-11-25.
Relies on Logging (deprecated) - log to stderr or use OpenTelemetry
No timeout specification or result limits documented. Tool calls external Gemini API with no explicit timeout in the code. A hung connection could block the MCP server indefinitely. No guidance on maximum response size or truncation behavior.
Output response may contain API credentials or sensitive metadata. The raw response from Gemini API is returned directly with no scrubbing. If the model API response includes tokens, session IDs, or internal system messages, these leak into the LLM context and logs.
No rate-limiting or usage tracking. Tool does not declare required permissions or rate limits. An agent in a retry loop could generate hundreds of Gemini API calls per minute, rapidly exhausting quota and accruing charges.
Hardcoded model 'gemini-2.5-pro' prevents flexibility. The tool does not accept a model parameter, forcing users to use a single (potentially expensive or unavailable) model. Future API deprecations or user preferences for faster/cheaper models cannot be accommodated.
analyze_youtube
Sanitize API responses before returning. Strip any 'token', 'session_id', 'credentials', or internal metadata fields from the Gemini response. Return only the analysis text and metadata relevant to the user.
Add an optional 'model' parameter with a default: 'model' (string, enum: 'gemini-2.5-pro', 'gemini-2.0-flash', 'gemini-1.5-pro', default 'gemini-2.5-pro'). Description: 'The Gemini model to use. Pro models offer higher quality; Flash offers faster responses and lower cost.' This allows users to trade quality for speed/cost.
Add rate-limit and quota awareness. Log the API quota usage and return a warning if usage exceeds 80%. Example: {'response': '<analysis>', 'quota_warning': 'You have used 85% of your daily Gemini API quota. Consider waiting before the next call.'} This helps agents recognize when they are approaching limits.
Document the tool as READ_ONLY with no side effects. Add to the description: 'This tool only reads and analyzes; it does not modify YouTube content, create comments, or change account settings.' This clarifies to the LLM that it is safe to call without destructive consequences.