The server provides a single tool (filter_tools) with a basic description and partial schema. Critical issues: (1) The tool name 'filter_tools' is generic and does not follow verb_noun conventions, a better name would be 'recommend_tools', 'rank_tools', or 'search_relevant_tools'. (2) The description is extremely minimal (97 characters) and lacks essential detail about WHEN to use this tool, WHAT it returns, and any prerequisites or context requirements. (3) The input schema is present but severely underspecified: the 'messages' parameter lacks format/structure documentation, 'available_tools' has no clarity on expected object shape or required fields, and 'max_tools' has no min/max bounds or default value. (4) There is NO documented output schema, the response structure is completely undocumented, forcing LLMs to infer what fields to expect. (5) No error handling guidance is provided. (6) The tool appears to be a filtering/ranking utility intended to reduce tool bloat for downstream LLM calls, but this intent is buried in the description and not surfaced clearly. (7) The implementation code visible (toolbench_evaluator.py) shows the tool is part of a complex evaluation pipeline with vector stores and embeddings, but none of this architectural context appears in the tool definition, leaving users/LLMs confused about dependencies or behavior.

• Rename 'filter_tools' to a more descriptive action verb. Recommend 'rank_tools_by_relevance' or 'recommend_relevant_tools' to signal that the tool both filters AND ranks by relevance to the conversation context.
• Expand the description to 150 - 300 characters explaining: (1) This tool analyzes a conversation and returns the top N most relevant tools from a candidate list. (2) Use this when you have many tools available and want to focus on the most contextually appropriate ones. (3) Returns a ranked list of tool objects sorted by relevance score (highest first).
• Add detailed parameter descriptions: 'messages' → 'Array of conversation messages. Each message should be an object with {role: "user"|"assistant", content: "text"}.'; 'available_tools' → 'Array of tool definitions to filter. Each must include {name: string, description: string}. Tool objects follow MCP tool definition format.'; 'max_tools' → 'Maximum number of tools to return (1 - 50, default: 10). Set to 0 to return all matching tools.'
• Document the output schema in the tool description: 'Returns an array of {name, description, relevance_score, reason} objects, sorted descending by relevance_score (0.0 - 1.0). The reason field explains why the tool was selected.'
• Add error handling guidance: 'If messages is empty, the tool returns all available_tools in random order with relevance_score 0.5. If available_tools is empty, returns empty array. If the embedding service is unavailable, falls back to BM25 keyword matching.'
• Consider renaming 'available_tools' parameter to 'tools_to_filter' or 'candidate_tools' for clarity (available vs candidate conveys different semantics).

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry