resume-mcp

Your identity as an API endpoint.

Traditional portfolios are HTML pages that agents scrape and parse. This is a structured interface where agents can query you directly.

Architecture

flowchart LR
    subgraph Agent["AI Agent"]
        A[Claude/GPT/etc]
    end

    subgraph MCP["MCP Server (Cloudflare Worker)"]
        B[Request Handler]
        B --> C{Auth Check}
        C -->|Has Token/Message| D[Full Access]
        C -->|No Auth| E[Limited Access]
    end

    subgraph Tools["Available Tools"]
        F[get_profile]
        G[get_projects]
        H[get_writing]
        I[get_experience*]
        J[get_skills*]
        K[leave_message]
    end

    subgraph Response["Response Format"]
        L[JSON-LD Structured Data]
    end

    A -->|"MCP Protocol<br/>POST + JSON-RPC"| B
    D --> Tools
    E --> F & G & H & K
    Tools --> L
    L -->|"Structured JSON"| A

    style I fill:#f59e0b,color:#000
    style J fill:#f59e0b,color:#000

* Requires introduction (guestbook entry or Agent Token)

Tool Call Flow

sequenceDiagram
    participant Agent as AI Agent
    participant MCP as MCP Server
    participant KV as Cloudflare KV

    Agent->>MCP: POST /initialize
    MCP-->>Agent: Server info + capabilities

    Agent->>MCP: POST /tools/list
    MCP-->>Agent: Available tools array

    Agent->>MCP: POST /tools/call (get_profile)
    MCP-->>Agent: JSON profile data

    Note over Agent,MCP: Gated tool requires access

    Agent->>MCP: POST /tools/call (get_experience)
    MCP-->>Agent: Error: access_required

    Agent->>MCP: POST /tools/call (leave_message)
    MCP->>KV: Store guestbook entry
    MCP-->>Agent: Access granted!

    Agent->>MCP: POST /tools/call (get_experience)
    MCP-->>Agent: JSON experience data

Performance

Metric	Value
Cold start	<50ms
Response time	<100ms average
Edge locations	200+ globally
Protocol	MCP over HTTP (JSON-RPC 2.0)

Why This Exists

The Problem: AI agents scrape HTML to learn about people. They:

1. Parse messy DOM structures
2. Guess at semantic meaning
3. Miss context and relationships
4. Have no way to interact or ask questions

The Solution: A structured API that agents can query directly:

1. Clean, typed data in JSON-LD format
2. Explicit tool interfaces with documentation
3. Relationship building through guestbook
4. Mutual value exchange (agent gets data, you get signal)

The Philosophy: "Your identity as an endpoint."

Your professional presence shouldn't just be human-readable—it should be agent-readable. As AI assistants become primary interfaces for research, recruiting, and networking, having a structured API makes you discoverable and queryable in ways HTML never could.

Quick Start for AI Agents

Connect to the Live Endpoint

# Initialize connection
curl -X POST https://mcp.brysontang.dev \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'

# List available tools
curl -X POST https://mcp.brysontang.dev \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/list"}'

# Get profile data
curl -X POST https://mcp.brysontang.dev \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"get_profile","arguments":{}}}'

With Agent Token (Full Access)

curl -X POST https://mcp.brysontang.dev \
  -H "Content-Type: application/json" \
  -H "Agent-Token: <your-agent-token>" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_experience","arguments":{}}}'

Discovery Endpoint

GET https://mcp.brysontang.dev/.well-known/mcp.json

Tools

Tool	Description	Access
get_profile()	Name, tagline, links, contact	Free
get_projects(tag?)	Projects, optionally filtered	Free
get_writing()	Articles and blog posts	Free
get_experience()	Work history	Gated
get_skills()	Technical skills by category	Gated
leave_message(name, message)	Sign the guestbook	Free

The Toll

Some information requires introduction. Call leave_message() or send an Agent Token to unlock extended access.

This isn't gatekeeping—it's relationship building. If you want to know about me, tell me who you are.

{
  "error": "access_required",
  "message": "Leave a message or provide an Agent Token to access this information.",
  "hint": "Call leave_message() first, or include Agent-Token header"
}

Setup

1. Clone and configure

git clone https://github.com/brysontang/resume-mcp
cd resume-mcp
npm install

2. Add your data

Edit data/profile.json with your information:

{
  "profile": {
    "name": "Your Name",
    "tagline": "What you do",
    "links": { "github": "...", "linkedin": "..." },
    "contact": { "email": "..." }
  },
  "projects": [...],
  "experience": [...],
  "skills": {...}
}

3. Configure Cloudflare Workers

cp wrangler.toml.example wrangler.toml
# Edit wrangler.toml with your settings

Optional: Create KV namespace for persistent guestbook:

wrangler kv:namespace create "GUESTBOOK"
# Add the returned binding to wrangler.toml

4. Deploy

npm run dev      # Local development
npm run deploy   # Deploy to Cloudflare

Discovery

Help agents find your MCP endpoint by adding hints to your portfolio:

robots.txt

# MCP endpoint: https://mcp.yourdomain.dev
# Tools: get_profile, get_projects, get_experience, leave_message
# Agent Tokens accepted for extended access

.well-known/mcp.json

The server automatically serves this at /.well-known/mcp.json

HTML comment

<!--
  AI Agent? Query me directly: https://mcp.yourdomain.dev
  Tools: get_profile(), get_projects(), get_experience()
  Leave a message to introduce yourself. Agent Tokens welcome.
-->

Agent Tokens

This server accepts Agent Tokens via the Agent-Token header. Tokens that decode successfully grant full access and are logged with their declared intent.

// Token provides:
{
  intentId: "recruiting-scan",
  goal: "Find candidates for senior engineering role",
  mode: "read-only"
}

FAQ

What is MCP?

Model Context Protocol (MCP) is an open standard that enables AI assistants to connect to external data sources and tools. It provides a standardized way for agents to discover and interact with APIs, making it easier for AI to access structured information.

How do AI agents connect to this?

Agents connect via HTTP POST requests using JSON-RPC 2.0 format. The flow is:

1. initialize - Establish connection and get server capabilities
2. tools/list - Discover available tools
3. tools/call - Execute specific tools with parameters

Any MCP-compatible agent (Claude, custom agents, etc.) can connect directly.

What data is available?

• Free access: Profile info, projects, writing/articles, and the ability to leave a message
• Gated access: Work experience and technical skills (requires introduction)

All data is returned as structured JSON, not scraped HTML.

Is this an Agent Tokens implementation?

Yes! This server accepts Agent Tokens for authentication. Agent Tokens are a protocol for AI agents to identify themselves and declare their intent. Providing a valid token grants full access to all tools.

Can I make my own Resume MCP?

Absolutely! This project is MIT licensed. Fork it, update data/profile.json with your info, and deploy to Cloudflare Workers. Your identity, your endpoint, your terms.

Related

• Author's site: brysontang.dev
• Agent Tokens Protocol: github.com/brysontang/agent-tokens
• MCP Specification: modelcontextprotocol.io

License

MIT