fetch_concepts

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

With no annotations, the description carries the full behavioral burden. It discloses that results are ranked with scores and snippets and supports multiple modes, but does not state whether the operation is read-only, any side effects, rate limits, or behavior of threshold parameter. Adequate but not comprehensive.

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?

Three sentences with no fluff, front-loaded with purpose. Could be more structured (e.g., list parameters), but good conciseness.

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 has 6 parameters and an output schema, the description covers basic functionality but lacks parameter details and usage context. Output schema exists, so return values don't need elaboration, but parameter semantics are incomplete.

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 mentions 'query' as natural language and 'mode', but fails to describe tags, type, top_n, and threshold parameters, leaving their semantics unclear.

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 searches the knowledge bundle using natural language queries, returns ranked results with scores and snippets, and supports multiple search modes. This distinguishes it from sibling tools like list_concepts (which likely lists all) and show_concept (for a single concept).

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 for natural language search but does not explicitly state when to use this tool versus alternatives like list_concepts or show_concept. No exclusions or when-not-to-use guidance is provided.

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