mimir_ask

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the readOnlyHint and destructiveHint annotations by detailing the internal process: recalling top-k entities, assembling context, and querying the configured LLM (Ollama) for an answer with cited sources. It also discloses the dependency on the '--llm-endpoint' configuration, which is critical for the tool's operation.

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 extremely concise: two short sentences. The first sentence clearly states the primary function, and the second adds important internal details and a requirement. No extraneous words; every sentence earns its place.

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?

For a tool with 2 parameters, full schema coverage, and an output schema (indicated by context), the description covers the key aspects: purpose, internal process (RAG, top-k, LLM), and a configuration requirement. It lacks explicit mention of which memories are queried (e.g., current workspace) but remains sufficiently complete for an agent to correctly invoke the tool.

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?

The input schema provides full descriptions for both parameters ('query' and 'top_k'), achieving 100% schema coverage. The description mentions 'Internally recalls top-k entities' which adds marginal context to the 'top_k' parameter but does not significantly enhance understanding beyond the schema. Given high schema coverage, the description adequately complements but does not surpass the baseline.

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: ask a natural language question and get a grounded answer from stored memories via RAG. It uses a specific verb ('ask') and resource ('stored memories'), and the wording distinguishes it from sibling tools like 'mimir_recall' or 'mimir_synthesize' by emphasizing the natural language Q&A nature.

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 mentions a prerequisite ('Requires --llm-endpoint to be set') but does not provide explicit guidance on when to use this tool versus alternatives (e.g., mimir_recall for raw retrieval, mimir_synthesize for generation without memories). The usage context is somewhat implied through the tool's purpose, but lacking explicit when-not-to-use or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.