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

Annotations already declare read-only and idempotent behavior. The description adds non-obvious context beyond annotations: the call is paid ($0.001 USDC or 1000 drops XRP) and catalog-backed. This informs cost expectations and data source. Not-found behavior is not covered, but this is minor for a simple lookup.

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 focused sentences front-load the core operation, then give the use condition, alternative tools, and cost. Every sentence earns its place with no repetition of schema 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?

With a complete input schema, rich parameter descriptions, an output schema, and helpful annotations, the description covers purpose, use case, alternatives, and cost. An agent has enough to decide when to invoke it and how to invoke it correctly.

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 coverage is 100%, and both parameters already have meaningful descriptions: symbol is 'API name or import path' with examples, and framework is defined with accepted library names. The description adds output-shape context, but it does not need to compensate for schema gaps.

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 precise operation: 'Map an API name to the current package, install line, import, and call shape.' It gives concrete examples and distinguishes the tool from siblings by framing it as resolving symbols whose location is unknown.

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 explicitly says when to use this tool — 'when you know the symbol but not where it lives' — and names alternatives with their preferred contexts: search_ai_framework_docs for concepts and fetch_latest_syntax for topic snippets. This gives clear routing guidance.

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