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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds substantial behavioral context beyond those: no auth/API key, no PII, rate limits (60 req/min, 250 calls/day), result shape ({data, meta}), meta fields (available, as_of, source_scope, etc.), and explicit guidance on handling meta.available=false (retry, don't report as zero). This is rich, non-contradictory behavioral disclosure.

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 well-structured and front-loaded: purpose, then use cases, then downstream integration, then return shape, then behavior. It is longer than average but every sentence carries information—rate limits, meta handling, and result semantics are all needed. It could be tightened slightly (e.g., some repetition of 'read-only' with annotations), but it earns its length.

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 6 optional parameters, a rich output schema, and many siblings, the description is exceptionally complete. It explains the return fields, the meta structure, error/retry behavior, rate limits, and how to chain results into other tools. It also clarifies the meaning of canonical keys vs. legal names. Nothing an agent needs to call it correctly is missing.

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% with detailed per-parameter descriptions and examples, so the baseline is 3. The description adds value by explaining the parameters' roles in the overall workflow (query for name resolution, sort for ranking, charter/state for filtering) and by clarifying result-field semantics (recent_volume is USD, keys roll up subsidiaries). It goes beyond the schema's literal parameter docs, though not dramatically.

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 ('Find'), a precise resource ('HMDA lenders'), and the core outcome ('resolve names to canonical lender keys'). It clearly distinguishes itself from siblings by positioning itself as the entry point ('Call this before any other lender tool') and by giving concrete example queries. This leaves no ambiguity about what the tool does.

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 provides explicit when-to-use guidance: 'Use for: turning a company name... into its canonical key, ranking lenders... or listing lenders by charter type or headquarters state.' It also gives when-not-to-use context ('Legal names such as... are not keys') and points to alternative tools by telling the agent to pass results to get_lender_profile, get_hmda_product_analytics, or get_servicer_analytics. This fully routes the agent to the correct workflow.

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