Katzilla MCP

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

The description adds valuable behavioral context beyond annotations: it discloses the data source (U.S. Office of Personnel Management — USAJobs), update frequency ('updates daily'), and return format details (Katzilla envelope with quality scores and citation metadata). Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description appropriately supplements with operational specifics without contradiction.

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 efficiently structured in two sentences: the first states the purpose and filters, and the second covers source, updates, and return format. Every element adds value (e.g., source credibility, update frequency, output structure), with no redundant or verbose phrasing, making it front-loaded and highly concise.

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 moderate complexity, rich annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage context, behavioral traits (source, updates), and output expectations, leaving no gaps for the agent to operate effectively without over-explaining structured data.

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?

With 100% schema description coverage, the input schema already fully documents all four parameters (keyword, locationName, payGradeFrom, limit). The description mentions filtering by 'keyword, location, salary, and agency', which aligns with but does not add meaning beyond the schema—salary maps to payGradeFrom, and agency is not a parameter. Thus, it meets the baseline for high schema coverage without significant enhancement.

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 ('Search') and resource ('current federal government job openings from USAJobs.gov'), and distinguishes it from siblings by specifying its unique domain (federal jobs) and source (USAJobs.gov). It explicitly identifies the agent context ('Government & Public Data Agent'), making its role unambiguous.

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 the tool (searching federal job openings) and lists filterable parameters (keyword, location, salary, agency), but does not explicitly mention when not to use it or name alternative tools for non-federal job searches or other government data. The sibling list includes many government data tools, but no direct alternatives for job searches are highlighted.

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