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

Since no annotations are provided, the description carries full burden and does an excellent job. It discloses that the tool normalizes data (JPY-style 100-unit quotes), the default date behavior (fillGaps carries last value across weekends/holidays), and the evidence attachment feature (source URL and content hash) for long-term defensibility. This goes far beyond a basic description and provides important behavioral insights.

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 three sentences, front-loaded with the core purpose, then providing essential detail without fluff. Every sentence adds value: purpose, current data scope/normalization, and optional parameters with their benefits. No redundant or tautological information.

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?

The description is complete given the tool's complexity: 6 optional parameters with good schema coverage, no output schema, no annotations. It explains the key behaviors an agent needs to know (catalogue listing, gap filling, evidence retrieval). The only minor gap is lack of explicit return structure, but no output schema exists, so the description's level is appropriate.

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 67%, so the description partially compensates. It explicitly names `seriesId`, `fillGaps`, and `includeEvidence` with meaningful context beyond the schema (e.g., what fillGaps does, why includeEvidence matters). It doesn't explain 'from'/'to' formats but the schema already provides patterns and descriptions, and 'limit' is self-explanatory. The additional context on seriesId (catalogue listing) is valuable.

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 returns 'the accumulated history of public data series with change statistics and a source receipt for every point.' It specifies the resource (public data series, specifically FX data) and the action (return history), distinguishing it from siblings like tr_fuel_prices or model_archive which likely serve different data types.

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 gives explicit usage context: 'Leave `seriesId` empty to list the catalogue' and explains the fillGaps and includeEvidence options for typical use cases. It doesn't explicitly state when not to use this tool vs alternatives, but given the diverse sibling tools, the unique functionality (time series history) makes usage clear.

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