pulse-mcp
Enforces build discipline for coding agents via a phase state machine, contract validation, and deterministic gate checks through MCP tools like pulse_next, pulse_submit, pulse_gate, and pulse_verify.
README
@agentpulselabs/pulse
Portable, contract-enforced build discipline for coding agents.
Pulse is a discipline engine — a phase state machine (Discovery → Stories → Architecture → Build → Deliver)
- a pluggable architecture contract + JSON schemas + a validator + a gate runner. It ships the brain; your coding agent (Claude Code, Cursor, Codex, Windsurf, Cline) provides the model, the UI, and the human.
The point: an AI agent driving Pulse produces code that is provably inside your platform's architectural
rules — because "done" is a deterministic gate command (npm run check), and a proposal that breaks an
invariant is rejected by code, not by review.
Formerly the Claude-Code-only "SEAP". Pulse ports the method to any host via MCP + a CLI plugin.
How it works — brain vs. nervous system
@agentpulselabs/pulse (BRAIN, pure) your coding agent (NERVOUS SYSTEM)
· phase state machine · runs the LLM (its model)
· contract (the rules) ── MCP ──▶ · renders progress (native UI)
· schemas + validator ◀── CLI ── · holds the human gate
· gate runner (npm run check) · edits files · owns PM-tool MCPs
Pulse never calls an LLM. pulse_next emits a work-list (prompts + a JSON schema per task); your agent
runs them on its model; pulse_submit validates the results against the schema and the contract;
pulse_gate locks decisions and advances; pulse_verify runs the gate command. The discipline travels; the
reasoning is the host's.
State lives in the repo (.pulse/)
Durable, git-tracked, travels across agents and humans, validatable offline:
.pulse/
pulse.config.json which contract governs + the gate command
engagement.json phase · status
board.json THE project board (machine truth)
BOARD.md rendered kanban (regenerated — don't hand-edit)
STATUS.md one-glance dashboard
sync.json optional PM-tool sync intent (Jira/GitHub/Asana)
decisions/product/ human decisions
decisions/architecture/ platform-dictated ADRs (each cites the invariant)
design/ working design docs (graduate to docs/DESIGN_*.md on gate)
Two ways to drive it
1. MCP (primary — every agent): run the pulse-mcp stdio server. Requires the optional peer dep:
npm i @agentpulselabs/pulse @modelcontextprotocol/sdk
Register it with your agent (example — Claude Code .mcp.json):
{ "mcpServers": { "pulse": { "command": "npx", "args": ["pulse-mcp"] } } }
Tools: pulse_start · pulse_next · pulse_submit · pulse_gate · pulse_verify · pulse_status · pulse_board_update · pulse_claim · pulse_sync.
2. CLI plugin (fallback): the published agentpulse CLI can install a pulse topic:
agentpulse plugins install @agentpulselabs/cli-plugin-pulse
agentpulse pulse start --requirements "…"
(The plugin is a thin shim that imports this core — see the parent design doc §4b.)
The contract is pluggable
// .pulse/pulse.config.json
{ "contract": "agentpulse", "gates": "npm run check && npm run check:types" }
contract: "agentpulse"→ the built-in reference contract (10 non-negotiables + 8 invariants + deep checks for table categories, migration-free schema, secrets-in-settings, no-DB-blobs).contract: "generic"→ point at your ownpulse.contract.md+ agatescommand. Pulse enforces schema-validity + the DAG + your gate; add your own deep checks with a JS contract (advanced).
What Pulse guarantees (and what it doesn't)
Guarantees (deterministic): schema validity, contract-invariant compliance, an acyclic work-item DAG, and that "done" = your gate command exited 0. Never delegated to an LLM.
Does NOT guarantee: insight. Reasoning quality is your host model's. On a weak model, Discovery is shallow — Pulse keeps it correct, not brilliant.
Develop
npm test # node --test — pure, no network, no MCP SDK needed
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.