> ## Documentation Index
> Fetch the complete documentation index at: https://void-2d69096f.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Tool Reference

> Detailed specification for the available AI Agent tools and expected JSON schemas.

## Available Tools

Our MCP server currently exposes one primary tool for autonomous AI agents.

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### `find_candidates_by_skill`

This tool allows an AI agent to scan a local directory of PDF resumes, pre-filter them for a specific technical keyword using Python, and extract deep structured data using Gemini 2.5 Flash.

#### Input Parameters

Agents must provide the following arguments when calling this tool:

**skill** `string` *required*

The specific technical skill to search for (e.g., "Java", "Python", "Spring Boot").

**directory\_path** `string` (default: ./resumes)

The local folder path where the PDF resumes are stored.

#### Expected Output

The tool enforces strict data validation via Pydantic. It returns a structured JSON array of candidates matching the requested skill.

**candidates** `array`

**Candidate Object Properties**

**full\_name** `string`

The candidate's extracted full name.

**role** `string`

The candidate's current or targeted job title.

**phone\_number** `string`

The extracted contact number.

**top\_5\_technical\_skills** `array of strings`

A list of the candidate's top technical proficiencies.

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## Testing with an Agent

If you are testing this integration inside an MCP-compatible client (like Claude Desktop), you can trigger the pipeline using a natural language prompt like this:

> *"Please use the `find_candidates_by_skill` tool to find all candidates who know 'Java' in the resumes folder. Present the results to me in a clean markdown table."*


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