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careerproof-mcp

careerproof-mcp

by detonnate
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Match job requirements to evidence

careerproof_match_requirements

Scores each job requirement against stored evidence, returning a match strength and the specific proof cited to show where a candidate fits or lacks support.

Instructions

Scores every requirement extracted for a job against all stored evidence (indexed repositories and/or CV claims), returning a match strength (strong / moderate / weak / unproven) plus the specific evidence cited for each requirement.

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": {
      "jobId": "<jobId>"
    },
    "name": "careerproof_match_requirements"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobIdYesJob ID returned by careerproof_analyse_job_description
projectIdNoRestrict matching to one project
candidateIdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the matching scope (all requirements vs all stored evidence), the granularity (per requirement), and the returned match-strength categories plus cited evidence. It does not state that the operation is read-only, whether results are persisted, or the cost of matching against all evidence.

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

Conciseness4/5

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

A single dense sentence that front-loads the action and keeps the return values at the end; every clause earns its place. It borders on run-on but wastes no words.

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

Completeness3/5

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

No output schema exists, and the description does cover the return shape, which is a plus. However, for a 3-parameter tool with an undocumented candidateId and no annotation coverage, it leaves the filtering semantics and operational side effects (persistence, read-only nature) unaddressed.

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

Parameters2/5

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

Schema coverage is only 67% and the description adds no parameter meaning at all. candidateId is undocumented in both places, and 'all stored evidence' mildly obscures the projectId/candidateId restriction options rather than explaining them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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

States a specific verb and resource: scoring every extracted job requirement against all stored evidence, and it names the output vocabulary (strong/moderate/weak/unproven). This clearly separates it from siblings like careerproof_find_evidence_gaps or careerproof_analyse_job_description, though no sibling is named explicitly.

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

Usage Guidelines2/5

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

The description gives no when-to-use, prerequisite, or alternative guidance. The only pipeline hint (jobId comes from careerproof_analyse_job_description) lives in the schema, not the description. An agent must infer that this is the step after job analysis.

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

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