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

The description reveals key behavioral traits beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, input cap of 200K chars with truncation warning, and output includes character offsets. This adds substantial value beyond readOnlyHint and idempotentHint annotations.

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 well-structured paragraph that front-loads purpose, then provides usage guidance, and ends with technical details. Every sentence adds value 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?

For a tool with no output schema, the description still mentions output contains passages with character offsets and similarity scores. While a full return structure isn't specified, it gives sufficient info for an agent to use the outputs. Input parameters and behavior are fully covered.

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 description coverage, the baseline is 3. However, the description adds meaningful context: explains query as 'natural-language query' with examples, specifies default/range for limit, and clarifies text max size. This goes beyond the schema's minimal descriptions.

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 performs 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes from sibling tools like ask_pipeworx_grounded by explaining the difference in usage patterns, making it easy for an agent to select appropriately.

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?

Explicitly advises to use when a record is too big for the prompt, explains how it saves context, and directly pairs with ask_pipeworx_grounded for a clear workflow. This provides strong guidance on when and how to use the tool vs alternatives.

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