ai.jakegaylor.com

An Express + TypeScript server that makes Jake Gaylor legible to AI systems. One deployment serves four interfaces to the same underlying resume and bio:

• A webpage (GET /) for humans
• Plain-text context (GET /llms.txt) for LLMs and crawlers
• An MCP server (/mcp) for AI clients a human has configured
• An A2A agent (/a2a + agent card) for agents that discover the site on their own

The resume content is fetched from jakegaylor.com/resume.json at boot and rendered to markdown, so every interface stays current without hand-editing.

Endpoints

Endpoint	What it serves
GET /	Webpage
GET /llms.txt	Resume + core beliefs as plain text
POST /mcp (+ GET/DELETE)	MCP over Streamable HTTP
GET /sse + POST /messages	MCP over legacy SSE transport
GET /.well-known/agent-card.json	A2A agent card (v1.0, with v0.3 translation for legacy clients), JWS-signed when a key is configured
GET /.well-known/jwks.json	Public keys for verifying the card signature
POST /a2a	A2A JSON-RPC endpoint (v1.0 + v0.3 compat)

MCP

Built on @jhgaylor/candidate-mcp-server. Tools exposed:

get_resume_text, get_resume_url, get_linkedin_url, get_github_url, get_website_url, get_website_text, contact_candidate (emails Jake), generate_interview_questions, assess_role_fit, get_candidate_preferences (structured screening data — see src/preferences.ts), get_availability + book_intro_call (Cal.com-backed scheduling with the same pending-confirmation and daily-cap guardrails as the A2A skill)

Connect a client to https://ai.jakegaylor.com/mcp, or run locally over stdio with npx @jhgaylor/me-mcp.

A2A

An A2A v1.0 agent built on @a2a-js/sdk, with the v0.3 compatibility layer enabled — clients that send no A2A-Version header are treated as v0.3 by the protocol, and most deployed clients still speak it. The card advertises both versions on the same URL.

Skills:

• about-jake — answers questions about Jake's experience using a cheap LLM grounded in the resume, bio, and screening data. If no LLM key is configured or the call fails, it falls back to returning the complete resume as markdown, so the skill contract holds either way.
• candidate-preferences — structured screening data (role types, level, location, relocation, remote, work authorization, comp stance, availability, resume links) returned as a JSON data part plus markdown. Triggered by screening/logistics keywords or metadata.skill. Values live in src/preferences.ts.
• assess-role-fit — send a job description (JD: prefix, or any long JD-shaped text) and get an honest LLM-graded fit assessment: verdict, strengths with resume citations, gaps named plainly, logistics check, and suggested interview questions. Falls back to returning the resume when no LLM is available.
• schedule-intro-call — scheduling-intent messages return open slots from the self-hosted Cal.com (cal.jakegaylor.com, public booking endpoints — no licensed API needed); BOOK: <slot> | <email> | <name> | <note> creates a booking. Guardrails: bookings require Jake's confirmation (Cal.com-native), explicit BOOK: prefix for the side effect, and a daily attempt cap. Config via CAL_* env vars in src/calcom.ts.
• connect-via-mcp — messages mentioning MCP get connection instructions for the richer MCP interface.
• contact-jake — messages starting with CONTACT: are relayed to Jake by email. Only that explicit prefix (or metadata.skill = "contact-jake") triggers mail.

Example:

curl -X POST https://ai.jakegaylor.com/a2a \
  -H "Content-Type: application/json" -H "A2A-Version: 1.0" \
  -d '{"jsonrpc":"2.0","id":1,"method":"SendMessage","params":{"message":{
        "messageId":"m1","role":"ROLE_USER",
        "parts":[{"text":"What is Jake's experience with Kubernetes?"}]}}}'

Configuration

Env var	Purpose
PORT	HTTP port (default 3000)
RESEND_API_KEY	Email via Resend SMTP (preferred when set)
EMAIL_FROM	From-address for Resend (domain must be verified in Resend)
MAILGUN_API_KEY	Email via Mailgun (fallback when Resend is not configured)
OPENROUTER_API_KEY	LLM for about-jake via OpenRouter (preferred when set)
OPENAI_API_KEY	LLM for about-jake via OpenAI directly (fallback)
LLM_MODEL	Model override (default openai/gpt-5.4-nano on OpenRouter, gpt-5.4-nano on OpenAI)
A2A_BASE_URL	Public base URL baked into the agent card (default https://ai.jakegaylor.com)
A2A_SIGNING_KEY_JWK	Private ES256 JWK (with kid) that signs the agent card; unset serves an unsigned card
POSTHOG_API_KEY	Server-side agent-traffic analytics (card fetches, MCP/A2A requests, skill routing); analytics are disabled without it
POSTHOG_HOST	PostHog endpoint (default https://us.i.posthog.com)

With no email keys set, contact_candidate/contact-jake report failure gracefully. With no LLM keys set, about-jake is fully deterministic.

Development

npm install
npm run build        # tsc
npm run dev          # stdio transport, auto-reload
npm run dev:web      # HTTP transport on :3000, auto-reload

src/
  ├── index.ts           # Entry point; picks stdio or HTTP transport
  ├── express.ts         # HTTP server: web, MCP, A2A mounting
  ├── a2a.ts             # A2A agent card, executor, skills
  ├── preferences.ts     # Structured screening data (edit values here)
  ├── stdio.ts           # STDIO transport for MCP
  ├── config.ts          # Server + candidate config; fetches resume at boot
  ├── resumeMarkdown.ts  # JSON Resume → markdown renderer
  └── types.ts           # Shared types

Deployment

Pushes to main trigger a GitHub Actions build of a multi-arch Docker image (jhgaylor/jake-gaylor-com-mcp-server). The workflow then pins k8s/kustomization.yaml to the new sha-<commit> tag and commits it back; Flux watches the repo and rolls the deployment on the home-cloud k3s cluster. Runtime secrets come from Infisical via an InfisicalSecret (see k8s/infisicalsecret.yaml) and land in the pod through envFrom.

License

MIT