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

Annotations already cover safety (readOnly, idempotent, openWorld, non-destructive), lowering the bar. The description adds concrete behavioral context: ranking is by popularity, coverage is 800k+ series. It omits pagination detail, which the limit param partly covers.

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?

Three compact clauses with the resource scope and routing condition front-loaded. No filler. Slightly terse given the tool's discovery role, but efficient.

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 search/discovery tool with full schema coverage and rich annotations, the description covers purpose, scope, ranking, and when to use it. Missing only edge-case context (e.g., that results lack values, which other fred_ tools supply), but sufficient to 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 description coverage is 100%, so the schema already documents all four parameters (search_text, search_type, tag_names, limit). The description adds no parameter-level detail beyond what the schema provides, so baseline 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?

States a specific verb+resource ('Full-text search across FRED's 800,000+ economic series') and clarifies the return (matching series IDs and titles). This distinguishes it from siblings like fred_series_info, fred_observations, and other fred_* tools that operate on known series.

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?

'Use when you don't know the exact series ID' gives a clear condition for selecting this discovery tool over the ID-based siblings. It doesn't name specific alternatives (e.g., fred_category_series) or state exclusions, but the routing intent is unambiguous.

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