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career-agent-mcp

career-agent-mcp

by leelakrishna288
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

analyse_job

Score job postings against your master profile using a weighted rubric to get an apply/maybe/reject decision, hard-reject reasons, and learnable versus critical gaps.

Instructions

Score a job posting against the master profile using a deterministic weighted rubric (technical 25, experience 20, AI relevance 15, backend 10, seniority 10, location 5, compensation 5, growth 5, learnability 5). Returns the score breakdown, an APPLY / MAYBE / DO_NOT_APPLY / MANUAL_REVIEW decision, hard-reject reasons, and gaps split into learnable versus critical.

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": "<job>"
    },
    "name": "analyse_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobYesJobPosting object.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
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 largely meets it: it discloses the deterministic weighted rubric with explicit weights, the decision taxonomy (APPLY/MAYBE/DO_NOT_APPLY/MANUAL_REVIEW), hard-reject reasons, and learnable-vs-critical gap classification. It does not address side effects, whether the master profile is fixed/implicit, or rate limits, but behavioral disclosure is unusually strong for a no-annotation tool.

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?

Two dense sentences that front-load the method (weighted rubric) before the outputs. The weight enumeration is long but earns its place by disclosing the scoring model; no filler sentences.

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?

No output schema exists, but the description compensates by enumerating return contents (breakdown, decision, hard-reject reasons, gap categories), which is what an agent needs to consume results. The nested JobPosting structure and the implicit master-profile dependency are the remaining gaps.

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%, so the single 'job' parameter is documented in the schema as a JobPosting object; the description adds no format or structural detail beyond that. One noteworthy implicit dependency — that scoring happens against a 'master profile' not passed as a parameter — is mentioned but not clarified. 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 and resource ('Score a job posting against the master profile') and names the exact scoring mechanism, making it clearly distinct from siblings like tailor_resume or track_application. An agent immediately knows this is the evaluation/scoring tool.

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?

Usage is implied by the scoring semantics (use it to decide whether to apply), but there is no explicit when-to-use statement, no when-not conditions, and no named alternatives among the siblings. The context is reasonably inferable but not spelled out.

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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