mcpField
R
resume-evidence-mcp

resume-evidence-mcp

Local
github.com/nimish14Business & Marketing0Updated 15w ago

About resume-evidence-mcp

resume-evidence-mcp is an MCP server for retrieving resume evidence relevant to a job description, for workflows that need grounded candidate facts. Its listed approach combines BM25 retrieval with evidence reranking.

Key features

  1. 01Retrieves resume evidence with BM25
  2. 02Reranks evidence
  3. 03Returns grounded resume facts for a job description

Use cases

  1. 01Find resume evidence relevant to a job description
  2. 02Give a coding agent grounded candidate facts

MCP support

Provides BM25 resume evidence retrieval tools to MCP clients, including coding agents.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"nimish14-resume-evidence": {
"command": "npx",
"args": [
"-y",
"resume-evidence-mcp"
]
}
}
}
Run commandnpx -y resume-evidence-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmresume-evidence-mcp0.1.0

Tools

2

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
evidence_strengthenumNoFilter by evidence strengthhigh | medium | low | all

Details

From the project README

Practical LLM Apps

Clone-and-run AI agents, RAG apps, and MCP servers.

Hand-built examples you can customize and ship — not slide-deck demos.

Works with OpenAI, and several apps run fully local with no API key.


Run one now

Invoice Follow-up Agent
git clone https://github.com/nimish14/practical-llm-apps.git
cd practical-llm-apps/starter_ai_agents/invoice_followup_agent
pip install -r requirements.txt
cp .env.example .env   # add OPENAI_API_KEY
streamlit run app.py
Chat with PDF
cd practical-llm-apps/starter_ai_agents/chat_with_pdf
pip install -r requirements.txt
cp .env.example .env   # add OPENAI_API_KEY
streamlit run app.py
Resume Evidence RAG (no API key)
cd practical-llm-apps/rag_apps/resume_evidence_rag
pip install -r requirements.txt
streamlit run app.py
Freelance Ops MCP (Cursor / Claude)
cd practical-llm-apps/mcp_servers/freelance-ops-mcp
npm install
npm run build
npx -y @modelcontextprotocol/inspector node dist/index.js

Add to Cursor MCP settings:

{
  "mcpServers": {
    "freelance-ops": {
      "command": "npx",
      "args": ["-y", "freelance-ops-mcp"]
    }
  }
}

(After npm publish — until then, point args at node + local dist/index.js.)


Browse templates

Starter AI Agents
AppDescription
Invoice Follow-up AgentDraft polite → firm invoice reminders with tone control and overdue context
Chat with PDFAsk questions about an uploaded PDF with page citations
RAG Apps
AppDescription
Resume Evidence RAGHybrid MiniLM + BM25 retrieval that returns grounded resume facts for a job description
MCP Servers
ServerDescription
freelance-ops-mcpTools for overdue status, tone suggestion, and invoice reminder drafts
resume-evidence-mcpBM25 resume evidence retrieval tools for coding agents

Project goals

  1. Every app: clone → install → run in under 2 minutes
  2. Clear README with problem, pipeline, and limits
  3. MCP servers packaged for npm + MCP Registry
  4. Prefer small, focused tools over mega-frameworks

License

MIT — fork it, ship it, sell it.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.