resolve_entity

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

No annotations provided, so description carries full burden. Mentions use of difflib and return values (best_match, candidates), but lacks details on case sensitivity, matching algorithm specifics, or any side effects. It implies read-only behavior but does not explicitly confirm safety or idempotency.

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, front-loaded with core purpose. Each sentence adds value: purpose, usage guideline, and return value. No redundant or filler content.

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 fuzzy-matching complexity and lack of annotations, the description covers purpose and usage well. It mentions the output schema exists (return values defined elsewhere). However, it omits behavioral details like case sensitivity and performance considerations, which would enhance completeness.

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 description must compensate. It clarifies the query parameter as a user-supplied string (e.g., fund name) but does not explain the column parameter, top_k, filename, or dataset_uri. This leaves significant ambiguity for a tool with 5 parameters.

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?

Description clearly states the tool's function: fuzzy-match a user-supplied string against real column values using difflib. It specifies the result includes best_match and candidates, and distinguishes its role as a preprocessing step for other tools. This clearly differentiates it from sibling search tools.

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 states 'Always call this before any tool that takes a fund name, type, or sector string' and explains the benefit of preventing 'not found' errors. This provides strong guidance on when to use the tool, effectively distinguishing it from sibling tools like search_funds.

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