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

Despite annotations (readOnlyHint=true, openWorldHint=true), the description adds substantial behavioral context: it explains the literal nature of Leboncoin searches, the multi-query strategy, filtering criteria (duplicates, accessories, broken units, etc.), and crucially warns that user-generated content is untrusted data and may contain prompt injection attempts. This exceeds what annotations convey.

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 longer than average but every sentence earns its place: it explains the mechanism (multi-query, literal search), the filtering logic, how to interpret `rejected`, and a critical security warning. It is front-loaded with the purpose and well-structured, though slightly dense.

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's complexity (26 parameters, no output schema), the description is remarkably complete. It covers the workflow, edge cases (literal search), how to interpret results (rejected field), and the prompt injection risk. It provides enough context for an agent to use the tool correctly and safely, without needing additional documentation.

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?

The schema already provides high coverage (88%) with detailed descriptions for each parameter. The tool description does not add per-parameter meaning but does contextualize how parameters like `queries`, `maxQueries`, and `minSimilarity` fit into the overall behavior (e.g., building phrasings, filtering). This is borderline, but since the schema does the heavy lifting, a baseline of 3 is appropriate.

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's purpose: it finds comparable listings for price estimation by building multiple search phrasings, pooling results, and filtering out irrelevant ones. It distinguishes itself from generic search tools (e.g., search_listings) by emphasizing the automated query construction and filtering for similarity assessment.

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 context ('behind every price estimate') and gives operational guidance (e.g., provide observed facts, read `rejected` before trusting results). It does not explicitly name alternatives or when not to use it, but the specialization is clear from the title and purpose. This earns a 4 for clear context without explicit exclusions.

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