What is the purpose of this server?
It gives LLMs access to candidate information (resume, profile URLs, website content) through the Model Context Protocol, intended as a library to embed in other applications.
@jhgaylor
基本情報
A Model Context Protocol (MCP) server library that gives LLMs access to information about a candidate.
Returns the candidate's resume content as text
Returns the URL to the candidate's resume
Returns the candidate's LinkedIn profile URL
Returns the candidate's GitHub profile URL
Returns the candidate's personal website URL
Returns the content from the candidate's personal website
Sends an email to the candidate (requires Mailgun configuration)
A Model Context Protocol (MCP) server library that gives LLMs access to information about a candidate. It is designed to be integrated into other applications, not run as a standalone service, and provides resources and tools for retrieving candidate resume content, URLs, and contact details.
Install via npm install @jhgaylor/candidate-mcp-server. Import createServer, configure with serverConfig and candidateConfig, then connect to an MCP transport (Stdio, Streamable HTTP, or Express). The library provides resource URIs like candidate-info://resume-text and tools like get_resume_text and contact_candidate.
It gives LLMs access to candidate information (resume, profile URLs, website content) through the Model Context Protocol, intended as a library to embed in other applications.
Pass a `candidateConfig` object to `createServer` containing fields like `name`, `email`, `resumeUrl`, and other candidate properties. The email is required for the `contact_candidate` tool.
Node.js 20+ and npm or yarn. No other required runtime dependencies are mentioned beyond the MCP SDK and Mailgun (optional).
Yes. The README demonstrates Stdio, Streamable HTTP, and Express transports. Transport choice depends on your integration environment.
Yes. The library is designed to be extended. Examples show adding custom resource classes and registering custom tools with Zod schema validation.
JiCo for Jira & Confluence: local MCP server for Jira Data Center - redacts personal data before the AI model, holds every write for approval
Set up and manage the Eddyter AI rich text editor by talking to your AI agent — for Claude, Cursor, Codex and other MCP tools. A Model Context Protocol server that lets AI tools add Eddyter to a React, Next.js, Vite or R
OrangePro local-first CLI + MCP server for behavior mapping, grounded test generation, and dynamic proof.
Modelglass turns "which model should I use?" into a lookup instead of a guess. Seven tools give your agent live pricing across image, language, video, and audio models. Sourced and versioned, not scraped once and forgott

Automated codebase documentation for GitHub: searchable docs, MCP context for AI agents, doc drift detection, and Friday Cleanup PRs.
コメント