search_blog

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

The description goes beyond annotations (readOnlyHint, idempotentHint) by detailing the TF-IDF ranking mechanism, a scoring threshold (0.001), determinism for a given knowledge base snapshot, and the behavior matrix. It clearly states the tool is pure read-only.

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 concise and well-structured: a one-line summary, followed by a behavior matrix table. Every sentence adds value, and there is no 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 moderate complexity, full schema coverage, and presence of an output schema, the description covers all necessary behavioral aspects (filtering, sorting, default behaviors). It could optionally mention result format or pagination, but the output schema presumably handles that.

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?

With 100% schema coverage, baseline is 3. The description adds significant value by explaining how query, sort, and tag interact (e.g., empty query defaults to date_desc, non-empty query uses relevance, etc.), which is not captured in the parameter descriptions alone.

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 that the tool searches the Sovereign AI Blog for articles matching a natural language query, with optional tag filtering and sorting. It distinguishes itself from sibling tools like list_tags (which discovers tags) and get_article (which retrieves a specific article), and the unrelated diagnose_sglang.

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 provides a 'Behaviour matrix' that explains how different parameter combinations behave, giving implicit usage guidance. It also explicitly suggests using list_tags to discover available tags. However, it does not directly compare when to use this tool versus get_article or diagnose_sglang.

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