Skip to main content
Glama

parse_cv

Extract structured candidate profile from a CV or resume, including name, skills, experience, job titles, education, and bio summary. Accepts PDF file path or raw text.

Instructions

Parse a CV/resume (PDF path or raw text) and extract a structured candidate profile: name, skills, years of experience, job titles, education, and a short bio summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cv_textNoRaw text content of the CV/resume
file_pathNoAbsolute path to a PDF CV/resume file
Behavior2/5

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

With no annotations, the description carries full behavioral burden. It mentions handling PDF path or raw text and extracting fields, but does not disclose error behavior (e.g., invalid file path), performance characteristics, rate limits, or whether the operation is read-only. Essential behavioral details are missing.

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 efficiently conveys the tool's function and output. Every word adds value, with no redundancy or unnecessary information.

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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description provides enough context for basic usage. It explains inputs and outputs. However, it could be more complete by specifying that exactly one of the two parameters is required and by mentioning error handling.

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 baseline is 3. The description adds context ('PDF path or raw text') but does not significantly enhance parameter understanding beyond the schema's descriptions. Both parameters are already well-documented in the schema.

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 purpose: 'Parse a CV/resume (PDF path or raw text) and extract a structured candidate profile.' It specifies the verb (Parse), resource (CV/resume), and output fields (name, skills, etc.). Additionally, it distinguishes itself from sibling tools, none of which parse CVs.

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: use this tool to extract structured data from a CV. However, it does not explicitly state when not to use it, nor does it mention alternative tools (e.g., score_job_fit might also process CVs). The guidance is adequate but lacks exclusion criteria.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/TheCodeDaniel/jobpilot-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server