JobDataLake MCP Server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the dataset scale (1M+ jobs, 20K+ companies) and search capabilities, but lacks critical behavioral details: whether this is a read-only operation (implied but not stated), pagination behavior beyond parameters, rate limits, authentication requirements, error handling, or response format. For a search tool with 14 parameters, this leaves significant gaps.

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: first establishes scope and scale, second enumerates capabilities. Every phrase adds value (scale numbers, search types, filter categories). Could be slightly more front-loaded by mentioning it's a search tool earlier, but overall well-sized without wasted words.

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 complex search tool with 14 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what results look like (structure, fields returned), pagination strategy beyond parameters, sorting options, performance characteristics, or error scenarios. The agent must rely heavily on the input schema alone, missing critical context for effective tool 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 79%, providing good baseline documentation. The description adds value by summarizing filter categories (location, salary, remote type, seniority, skills) and mentioning AI semantic search capability, which helps contextualize parameters like 'semantic_query'. However, it doesn't explain parameter interactions, default behaviors beyond schema defaults, or special syntax requirements beyond what's in schema descriptions.

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 job listings with specific capabilities (keyword search, AI semantic search, filters). It distinguishes from siblings like 'find_similar_jobs' by emphasizing broad search across 1M+ listings rather than similarity matching, and from 'get_job' by focusing on search rather than retrieval of a specific job. However, it doesn't explicitly contrast with 'get_company', leaving some ambiguity.

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 implies usage for searching job listings with various filters, but doesn't explicitly state when to use this tool versus alternatives like 'find_similar_jobs' (which likely finds similar jobs to a given one) or 'get_job' (which retrieves a specific job by ID). No guidance on prerequisites, error conditions, or performance considerations is provided.

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