search

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

Annotations already establish the tool as a safe read-only, idempotent operation, and the description adds important behavioral detail: matching is case-insensitive on the text layer, scanned pages cannot match, and snippets are limited to the first twenty matching pages. This is substantive context beyond the 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 front-loaded with the core behavior and then adds precise output and matching details. Every sentence contributes useful information without repetition or padding.

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 simple two-parameter schema, rich annotations, and the existence of an output schema, the description is complete. It covers what is searched, matching limitations, snippet limits, and the downstream extraction workflow.

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 explain parameter meaning, and it does: path is the PDF's absolute path on this machine, and query is the text to look for as written. It lacks syntax or format edge-case guidance, but both required parameters are meaningfully documented.

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 verb+resource: searching a local PDF's pages for a query and returning snippets. It distinguishes itself from extraction siblings by describing a search/match operation rather than full-document extraction.

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?

It gives a clear usage context by explaining that snippets help an agent decide which pages to read in full with extract_markdown. It does not explicitly contrast itself with extract_text or other siblings, but the intended workflow is clear.

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