bls_series_data

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful scope context (data categories and frequencies) but does not disclose behavioral traits like rate limits, API key advantages, or response shape. No contradiction with annotations.

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 front-loaded with a clear purpose sentence, followed by scannable bullets of example IDs and prefixes. It is longer than strictly necessary, but every bullet earns its place by aiding series ID selection, and the structure is easy to parse.

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 no output schema, the description partially covers return expectations ('monthly/quarterly/annual observations') but not the response structure (e.g., year/period/value fields). It also does not point to bls_search_series for unknown IDs, and the 10/20-year range limitation lives only in the schema, not the description. These gaps keep it from being fully complete.

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%, so the baseline is 3. The description adds real value by mapping series ID prefixes (CES, LNS, CU, WP, OE, JT) to data categories and providing six concrete example IDs, which helps an agent construct valid series_ids beyond the schema's generic 'comma-separated BLS series IDs'.

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 uses a specific verb ('Fetch') with a clear resource ('time series data from the Bureau of Labor Statistics') and states the return content (monthly/quarterly/annual observations for employment, wages, prices, and more). It is distinct from generic list tools but does not explicitly name sibling tools like bls_search_series or bls_cpi_breakdown, so it falls short of a 5.

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 popular series IDs and prefix table imply this tool is for fetching known BLS series by ID, which is useful context. However, there is no explicit when-to-use vs. alternative guidance—no mention that bls_search_series should be used to discover IDs, or that bls_cpi_breakdown covers subcomponent breakdowns. Clear context but no exclusions.

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