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parse_cv

Extract contact fields from pasted CV text using regex. Returns candidate fields, flags unknowns for confirmation, and does not write the profile until human approves.

Instructions

Zero-LLM regex extraction of contact fields from pasted CV text. Returns candidate fields (None = unknown — ask the human, never invent). Does NOT write the profile: CONFIRM with the human, then call update_profile.

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": {
      "cv_text": "<cv_text>"
    },
    "name": "parse_cv"
  }
}

Input Schema

Name	Required	Description	Default
`cv_text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 13, 2026

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations, the description discloses key behavioral traits: it is deterministic (regex-based, zero-LLM), returns candidate fields, treats None as unknown, and explicitly states it does not persist/write. This exceeds expectations by revealing its non-mutating nature and conservative fallback behavior.

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?

Three concise sentences, each adds value: purpose, return semantics, and workflow guidance. No fluff or redundancy.

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

Completeness5/5

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

For a one-parameter tool with an output schema, the description covers operation, return conventions, and integration with update_profile. It fully answers how to use the tool safely and effectively.

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

Parameters4/5

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

Schema coverage is 0% and the only parameter cv_text has no schema description. However, the description implies its meaning via 'pasted CV text.' It clarifies that input is raw pasted text, which is sufficient given a single required parameter.

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 a specific verb+resource: 'Zero-LLM regex extraction of contact fields from pasted CV text.' It distinguishes from siblings by explicitly noting it does NOT write the profile, unlike update_profile.

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

Usage Guidelines5/5

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

Provides explicit when-to-use and when-not-to-use logic: 'Does NOT write the profile: CONFIRM with the human, then call update_profile.' Also instructs to ask the human when None is returned, never inventing data. This is strong usage guidance versus alternatives.

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