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careerproof-mcp

careerproof-mcp

by detonnate
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Generate a cited STAR-answer outline

careerproof_generate_star_answer

Builds a Situation/Task/Action/Result outline for a competency from stored evidence like commits, PRs, and docs, listing gaps explicitly so clients can turn it into prose.

Instructions

Builds a Situation/Task/Action/Result outline for a competency and project, scaffolded entirely from stored evidence (commits, PRs, docs). Never invents narrative detail: gaps are listed explicitly in 'missing_information' rather than filled in. The connected client should turn this outline into flowing prose.

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": {
      "competency": "<competency>",
      "project": "<project>"
    },
    "name": "careerproof_generate_star_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectYesThe indexed project name to draw evidence from
competencyYesThe competency or question to answer, e.g. 'Describe a time you designed a complex solution'
maximumWordsNoTarget maximum length for the final prose answer

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
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 disclose a meaningful behavioral trait: it 'Never invents narrative detail' and surfaces gaps via 'missing_information.' That is valuable anti-hallucination context. It omits prerequisites (e.g., project must be indexed) and any auth/rate-limit context, which keeps it from a 5.

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?

Four compact sentences, front-loaded with what the tool builds, followed by its non-invention guarantee and the downstream handoff. No sentence is filler.

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?

For a generation tool with no output schema, the description usefully names a return field ('missing_information') and the outline structure, which compensates for the absent output schema. It stops short of stating prerequisite state (project indexed, evidence added) that an agent would need to call it successfully.

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 description coverage is 100%, so the schema already documents all three parameters with clear semantics. The description reinforces the competency/project pairing but adds no syntax or formatting detail beyond the schema, and maximumWords is not addressed in prose. 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 (Builds) and resource (Situation/Task/Action/Result outline), names its inputs (competency, project), and specifies the evidence source (commits, PRs, docs). This clearly distinguishes it from siblings like find_evidence or generate_interview_questions.

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?

Describes the pipeline role explicitly: 'The connected client should turn this outline into flowing prose,' which tells the agent where this tool sits relative to downstream work. However, it does not contrast with siblings like score_interview_answer or find_evidence, so the when-not-to-use case is left implicit.

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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