Vector search

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals reliance on a configured vector backend and an indexed corpus, but does not explain return format, ranking behavior, error cases, or what happens when the backend is missing or unconfigured.

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 with no filler and the core action front-loaded. It is concise, though its brevity contributes to under-specification in other dimensions.

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 no annotations, no output schema, and many similar sibling tools, the description is not complete enough. An agent cannot confidently determine when to choose this over other search tools, what results to expect, or what assumptions about the vector backend are safe.

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 for parameter meaning, but it does not. It never clarifies how 'query' should be interpreted (e.g., natural language, semantic text) or what 'limit' controls beyond the schema's basic definition.

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 states a specific action ('Search') and resource ('indexed paper vectors'), making the core purpose clear. The mention of a 'configured vector backend' hints at semantic/vector search and loosely distinguishes it from keyword-style tools like search_papers, though it does not explicitly name an alternative.

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?

There is no guidance on when to use this tool versus search_papers or other sibling tools. The description implies a vector-based search but does not state when that is preferred, when it is not, or what distinguishes it from alternative search tools.

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