resume-tailor-mcp

An MCP server that tailors a resume to a job posting. It exposes three tools to any MCP client (Claude Desktop, an IDE, the MCP Inspector) and supports two backends: the Anthropic API with an API key, or key-less operation using MCP sampling to borrow the host's model.

What MCP is

MCP (Model Context Protocol) is a standard for giving an LLM access to tools and data. A host (such as Claude Desktop) embeds the model, a server such as this one exposes capabilities, and the two communicate over JSON-RPC. The server has no model of its own and only answers requests.

Related MCP server: hrmcp-server

Tools

A ping health check, plus three tools:

The system prompt constrains the model to rephrase and re-emphasize existing resume content and to flag genuine gaps rather than fabricate experience.

Backends

The backend is selected with the TAILOR_MODE environment variable:

• api (default): calls the Anthropic API with an ANTHROPIC_API_KEY, using structured outputs so the model is constrained to the response schema.

• sampling: holds no key. It requests a completion from the host via MCP sampling (createMessage) and validates the returned text against the same schema. Requires a host that supports sampling, such as Claude Desktop.

The tools are identical across both backends. See Design notes for the tradeoff.

Requirements

• Node 20 or newer.

• The api backend requires an ANTHROPIC_API_KEY.

• The sampling backend requires a host that supports MCP sampling.

Installation

git clone https://github.com/mr-martinsosa/resume-tailor-mcp.git
cd resume-tailor-mcp
npm install
npm run build     # compile TypeScript to dist/

Usage

Run the test suite (uses injected fakes, so no API key, network, or cost):

npm test

Inspect the live server with the MCP Inspector:

npm run inspect

Claude Desktop

After npm run build, add the following to claude_desktop_config.json, using an absolute path:

{
  "mcpServers": {
    "resume-tailor": {
      "command": "node",
      "args": ["/absolute/path/to/resume-tailor-mcp/dist/server.js"],
      "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
    }
  }
}

For key-less operation, omit ANTHROPIC_API_KEY and set the mode instead:

"env": { "TAILOR_MODE": "sampling" }

Project layout

src/
  server.ts            boots the stdio server; selects the backend by TAILOR_MODE
  schema.ts            zod schemas and prompts (one schema per tool)
  tools/               one file per tool: registration and provider call
  llm/
    anthropic.ts       direct-API backend (structured outputs)
    sampling.ts        key-less backend (MCP sampling + client-side validation)
test/                  smoke, tool, and sampling tests, all run without a key
study-materials/       notes on MCP and the design

Design notes

• Structured outputs: the API backend uses messages.parse with zodOutputFormat, so the model is constrained to the schema and the SDK returns a validated, typed object with no manual parsing step.

• Single schema per tool: each tool's zod schema serves as the Anthropic output format, the MCP outputSchema, the prompt instruction in sampling mode, the client-side validator, and the TypeScript type.

• Provider seam: tools call an injected function (TailorFn, ScoreFn, ExtractFn) rather than the LLM directly. Tests inject fakes (so no key is needed), and the sampling backend was added without changing any tool.

• Backend tradeoff: the direct-API backend enforces the schema server-side but requires a key; the sampling backend is key-less but gives up server-side enforcement, so it validates the returned text itself.

License

MIT. See LICENSE.