job-mcp

by zvieli

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

job_fetch

Fetch a single canonical job by opaque ref from job_search and return factual retrieval status.

Instructions

Fetch one canonical job using an opaque ref returned by job_search.

Returns factual retrieval status only.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ref": "<ref>"
    },
    "name": "job_fetch"
  }
}

Input Schema

Name	Required	Description	Default
`ref`	Yes	Opaque, versioned JobRef string (e.g. 'v1_...').

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that only factual retrieval status is returned, which is useful, and that 'ref' is opaque and versioned. However, it does not mention permissions, error behavior, or whether the result includes full job data or just status, leaving gaps for a retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the core action and followed by the return caveat. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one required parameter, an output schema exists (so return values need not be described), and the description covers purpose, source of input, and return nature. It is nearly complete, missing only minor behavioral details like error handling or authentication, which are not critical here.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema describes 'ref' as an opaque, versioned JobRef string with an example. The description adds no parameter meaning 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Fetch) and resource (one canonical job) with scope (single, by ref). It explicitly differentiates from job_search by naming the sibling and clarifying that this retrieves one job using a ref that job_search returns.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear context: use after job_search to retrieve a specific job. It identifies the prerequisite (ref from job_search) but does not state exclusions or alternatives for fetching job details (e.g., get_linkedin_job_details), leaving some ambiguity about when to use another tool.

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