JobDataLake MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the method ('AI vector similarity') but lacks details on performance (e.g., speed, accuracy), limitations (e.g., data freshness, similarity thresholds), or output format (e.g., list structure, fields returned). For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 and adds a brief use case note in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it highly efficient and well-structured.

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 (AI-based similarity search) and lack of annotations and output schema, the description is incomplete. It explains the purpose and method but misses behavioral details (e.g., what 'similar' means, response format) and usage nuances. This is adequate as a minimum viable description but has clear gaps for effective agent use.

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 both parameters (job_id and per_page) adequately. The description does not add any additional meaning beyond what the schema provides, such as explaining how job_id is used in similarity calculations or constraints on per_page values. Baseline 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Find jobs similar to a given job listing') and the method ('using AI vector similarity'), distinguishing it from sibling tools like get_job (retrieve single job) and search_jobs (general search). It explicitly mentions the use case ('more like this' discovery), making the purpose highly specific and differentiated.

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 ('Great for "more like this" discovery'), implying it's for similarity-based recommendations rather than direct retrieval or general search. However, it does not explicitly state when not to use it or name alternatives like search_jobs for different query types, which would be needed for a perfect score.

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