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interview-prep-mcp

interview-prep-mcp

by shenmali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

match_cv_to_job_tool

Match a candidate's CV to a job posting and receive a compatibility score, strengths, and gaps.

Instructions

Match a CV against a job posting. Returns score (0-100), strengths, gaps, summary.

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": {
      "job_id": "<job_id>"
    },
    "name": "match_cv_to_job_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idYes
cv_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description bears full responsibility for behavioral disclosure. It indicates the tool returns a score, strengths, gaps, and summary, which is helpful. However, it does not state whether the tool has side effects (e.g., writes to a database) or disclose any other behavioral traits like required permissions or rate limits.

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?

The description is a single, front-loaded sentence that succinctly conveys the purpose and output. Every word contributes value, with no redundancy.

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?

The tool has low complexity with two parameters and an output schema covering return values. The description provides a high-level summary but fails to explain parameter semantics or how the tool fits into a workflow (e.g., relationship with parse_cv_tool). It is adequate but not thorough.

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

Parameters1/5

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

Schema description coverage is 0%, meaning the description must explain the parameters. The description entirely omits any mention of job_id or cv_id, leaving the agent to infer their meaning from the name alone. The schema indicates job_id is required and cv_id optional, but the description adds no explanatory value.

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?

The description clearly states the tool's function: 'Match a CV against a job posting.' It also specifies the output: score, strengths, gaps, summary. This differentiates it from sibling tools like parse_cv_tool and parse_job_posting_tool, which focus on parsing rather than matching.

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

Usage Guidelines3/5

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

The description implies usage when a match between a CV and job posting is needed, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites (e.g., CV and job must be parsed first). Context is implicit rather than explicit.

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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