search_series

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

With no annotations, the description carries the full burden. It discloses the search-and-return behavior, ordering, and the practical mapping from natural language to series_id. It stops short of describing the exact return structure, but the output schema likely covers that. This is solid for a non-destructive search tool.

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 concise and well-structured: a lead sentence, a usage context sentence, a result-ordering note, and a clear Args list. Every sentence adds value, no redundancy or fluff.

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?

Given the simple two-parameter input, an output schema that likely defines return values, and clear guidance on purpose and usage, the description is complete. It gives the agent all needed context to invoke the tool 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 0%, so the description fully compensates. It explains query as free-text search with examples, and limit as max number of series with a range (1-50). This adds meaning well beyond the bare schema properties.

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 searches FRED for data series matching a query, with a specific verb ('Search'), resource ('FRED'), and scope ('economic data series'). It also distinguishes itself from siblings by indicating its role in converting natural language to a series_id for get_observations.

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?

Explicitly says 'Use this first' and names the downstream consumer (get_observations), providing clear when-to-use guidance. It also mentions ordering by popularity, which helps set expectations. No exclusions needed for a search tool.

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