Katzilla MCP

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies the data source ('Federal Reserve Economic Data (Public Domain)'), update frequency ('updates daily'), and return format details ('Katzilla envelope { data, quality, citation }' with quality scores and citation info). No contradictions with annotations are present.

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 the core purpose in the first sentence, followed by supporting details in a structured manner. Every sentence adds value: the first defines the action, the second lists examples and scope, the third specifies source and updates, and the fourth explains the return format. There is no wasted text, making it highly 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?

Given the tool's complexity (data fetching with multiple parameters), rich annotations (covering safety and idempotency), and the presence of an output schema (implied by 'Returns the Katzilla envelope'), the description is complete. It adequately explains the tool's purpose, behavior, and output without needing to detail return values, as the output schema handles that.

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?

The input schema has 100% description coverage, clearly documenting all four parameters (seriesId, limit, startDate, endDate) with examples and constraints. The description does not add any additional meaning or clarification about the parameters beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.

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's purpose with a specific verb ('Fetch'), resource ('time-series observations from the Federal Reserve Economic Data (FRED) API'), and scope ('GDP, unemployment, CPI, and thousands of other economic indicators'). It distinguishes itself from sibling tools like 'economic__fred-search' by focusing on fetching observations rather than searching for 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?

The description provides clear context for when to use this tool ('Fetch time-series observations'), but it does not explicitly state when not to use it or name alternatives. It implies usage for economic data retrieval but lacks explicit exclusions or comparisons to other data-fetching tools in the sibling list.

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