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

With no annotations, the description carries the full burden and does well: it discloses the calculation methodology, the lookback window semantics, the revision log behavior, and the $0.12 USDC cost settled via x402. It does not cover error handling or exact response structure, but it goes well beyond a minimal description.

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 four sentences, each carrying distinct value: scope, returned content, methodology, and parameters/cost. It is slightly dense but not wasteful, and it front-loads the core purpose before details.

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 no output schema, the description lists the main result categories and the pricing model, and the schema fully handles parameters. It lacks exact JSON field names and any notes on empty results or error behavior, but an agent has enough to decide and invoke 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%, with both parameters documented including the 6-digit ticker format, the window enum, and default. The description adds the cost and restates the window options, but otherwise does not add meaning beyond the schema, so the baseline of 3 applies.

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 identifies the resource as analyst consensus for a single KRX ticker and enumerates the exact metrics returned (mean/median/min/max target price, spread, rating distribution, revision log). It distinguishes itself from broad research_latest by scoping to one ticker, though it does not explicitly name any sibling alternative.

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 conveys the core use case—get consensus across brokerages for one KRX ticker—and explains the methodology (each house's latest view, so weekly publishers don't outvote quarterly ones). However, it does not explicitly state when not to use it or contrast it with siblings like research_company_ticker or research_latest, leaving some selection reasoning to inference.

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