Retrieves context from a local knowledge base and answers your question with a cited Gemini response using RAG. Requires GEMINI_API_KEY.
Responde una pregunta con RAG sobre la base local: recupera contexto y genera una respuesta con Gemini, citando los archivos fuente. Necesita GEMINI_API_KEY configurada.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It discloses that the tool uses RAG, generates with Gemini, cites sources, and requires GEMINI_API_KEY. It does not explicitly state whether the operation is read-only or describe error handling for missing keys or empty results, but the core behavior is transparent enough for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the main action and then adds the prerequisite. Every clause adds value: the retrieval, generation, citation, and API key requirement are all included without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (RAG pipeline with external API) and the existence of an output schema, the description covers the essential call-time information: the question input and the required environment variable. It doesn't describe potential failure modes or the exact answer format, but the output schema likely covers return values, so the description is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only clarifies that the tool 'answers a question', directly linking the 'question' parameter to its purpose, but adds no further details such as format, length limits, or examples. Minimal compensation is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it answers a question using RAG, retrieves context, generates a response with Gemini, and cites source files. This is a specific verb-resource pair that distinctly separates it from siblings like search_knowledge_base (which likely returns raw matches) and reindex_knowledge_base (which rebuilds the index).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: it is for generative question answering with citations, as opposed to raw search or reindexing. However, it does not explicitly mention alternatives or give when-to-use/when-not-to-use guidance, leaving the differentiation to inference from the siblings' names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.