OrchestrateKit MCP
Plans workflows that integrate with Gmail for email operations such as sending, reading, and managing emails.
Plans workflows that integrate with Slack for messaging, channel management, and notifications.
Plans workflows that integrate with Stripe for payment processing, subscription management, and invoice handling.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OrchestrateKit MCPDesign a workflow for a customer support chatbot"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OrchestrateMCP
An evidence-backed workflow-design advisor for AI agents. Connect it to ChatGPT, Claude (web), Cursor, or Claude Desktop and it plans safer, more grounded AI workflows — grounded in a registry of tested components, edges, and golden-path playbooks. Read-only, stateless, holds no secrets.
Status: hosted health_check reports 64 components, 151 edges, 4 workers, 1 stack, 13 routes, 14 playbooks, and 18 tools; available over stdio and as a free hosted endpoint (https://mcp.orchestratemcp.dev/mcp).
What it does
OrchestrateMCP exposes a structured registry of:
components → the building blocks of AI workflows
edges → tested relations between components (requires, safer_with, conflicts_with, …)
stacks → opinionated technology choices for different deployment contexts
routes → tested paths through the component graph
playbooks → golden-path routes with full implementation guidanceWhen a user describes a workflow goal, the MCP can:
Match the goal to required capabilities and components.
Traverse tested component relationships.
Reuse sections of known golden-path playbooks.
Compose a candidate route when no exact playbook exists.
Score route confidence (coverage, tested edges, stack fit, safety, simplicity).
Return the route as structured implementation context for Cursor or Claude.
Related MCP server: dltHub-AI-workbench
What works right now
MCP server runs on stdio (Cursor, Claude Desktop) and over Streamable HTTP / a Cloudflare Worker (ChatGPT, claude.ai) — 18 registered tools.
health_checkreturns{ name, version, registry: { component_count, edge_count, stack_count, route_count, playbook_count, worker_count, untested_edge_pct } }.Hosted registry: 64 components, 151 edges, 1 stack, 13 routes, 14 playbooks, 4 workers.
Coverage accounting reports unmatched demand and unsupported supply instead of silently pretending the graph covers everything.
Corpus regression tests and release-trust floors ratchet the registry forward in CI.
pnpm verify(typecheck + lint + tests) passes from a clean clone and install.
Why trust this
OrchestrateMCP is stateless, read-only, holds no secrets, and makes no LLM calls inside its tools. Plans are composed from registry YAML, provenance tags mark computed fields, coverage accounting calls out unsupported pieces, and corpus contracts plus release-trust checks run in CI to catch drift.
Requirements
Node.js ≥ 20
pnpm
Local setup
cd orchestratekit-mcp
pnpm install
pnpm verify # typecheck + tests — must pass before anything else
pnpm dev # starts the MCP server on stdioThe server reads from stdin and writes JSON-RPC to stdout. All log output goes to stderr.
Connect from Cursor
Copy examples/cursor-mcp.json content into your Cursor workspace MCP config at .cursor/mcp.json. Replace the cwd value with the absolute path to this directory.
{
"mcpServers": {
"orchestratekit": {
"command": "npx",
"args": ["tsx", "src/server.ts"],
"cwd": "/absolute/path/to/orchestratekit-mcp"
}
}
}Connect from Claude Desktop
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"orchestratekit": {
"command": "npx",
"args": ["tsx", "src/server.ts"],
"cwd": "/absolute/path/to/orchestratekit-mcp"
}
}
}Connect from ChatGPT or claude.ai (hosted)
No install, no terminal — point your AI client at the free hosted endpoint:
https://mcp.orchestratemcp.dev/mcpFull walkthrough (ChatGPT Developer-Mode connector + claude.ai): docs/CHATGPT_USAGE.md.
Try This First
After connecting, paste this wrapper plus one starter goal. The default response should be a short product card: title, route, steps, connections, safety note, build controls, and four continuation choices.
Use the orchestratekit MCP tools.
Goal: [paste one starter goal here]
Call plan_workflow with this goal and render the returned summary_markdown
verbatim, including the A) B) C) D) continuation menu.Starter goals:
Build an agent that checks 5 competitor pages every morning, detects price changes, and sends me a Slack summary. I want to approve before anything external is changed.Build an agent that reads new leads from Gmail, drafts a reply, updates the CRM, and alerts sales in Slack after approval.When a pull request opens on GitHub, review the diff for bugs and risky changes, notify reviewers with a summary, and never edit or commit code.When a PDF invoice arrives in the shared AP Gmail inbox, extract totals and line items, match against purchase orders, notify AP in Slack for discrepancies, and hold every invoice for human approval before accounting.Use a content brief to generate social copy variants and a design brief, send it to a reviewer for approval, then publish externally only after approval.More first-run starters and expected output shapes: docs/FIRST_RUN_STARTERS.md.
Connecting your workflow's services
Connecting the MCP to your client takes no auth (it's a read-only advisor). But the workflows it plans need your credentials for Gmail, Slack, Stripe, your CRM, and so on. For how to provision those safely — least-privilege scopes, secret managers, and managed-auth brokers — see docs/CONNECTION_SETUP.md. OrchestrateMCP never holds a credential.
Scripts
Script | Description |
| Run server directly with tsx (no build step) |
| Compile to |
| TypeScript type-check only (no emit) |
| Run unit tests with vitest |
| Create a source-only review zip at |
| Generate bundles, typecheck, lint registry, run tests, and check release trust |
For source review packages, never zip the working folder directly. Use
pnpm export:safe; see docs/SAFE_EXPORT.md for the
forbidden paths and archive inspection command.
Project structure
orchestratekit-mcp/
src/
server.ts Entry point — wires MCP server to stdio transport
config.ts Server name and version constants
tools/
index.ts Tool registration (18 tools: health_check + 17 graph/advisor tools)
composeWorkflowRoute.ts
listGraphComponents.ts / getGraphComponent.ts
listGraphEdges.ts / getGraphEdge.ts
getStackRecommendation.ts
listKnownRoutes.ts / getRoute.ts
registry/
registryLoader.ts YAML loader with validation, status filtering, cross-ref checks
componentSchema.ts / edgeSchema.ts / stackSchema.ts / routeSchema.ts / playbookSchema.ts
registryTypes.ts / registryValidation.ts
graph/
capabilityMatcher.ts Keyword + token matching: goal text → components
routeComposer.ts Orchestrates all graph modules into a composed route
routeScoring.ts Deterministic 0-100 score with breakdown
routeOrdering.ts Topological sort via Kahn's algorithm
safetyAugmenter.ts Auto-adds approval gates and audit log
playbookOverlap.ts Detects overlap with known playbooks/routes
docs-index/ Supplementary docs loader (future)
lib/
errors.ts McpToolError class and toErrorResult helper
logger.ts Stderr-only logger (stdout reserved for transport)
registry/
components/ component YAML files (64 active)
edges/ edge/relation YAML files (151 active)
stacks/ stack YAML files
routes/ route YAML files (13)
playbooks/ golden-path playbook YAML files (14)
docs-index/ Supplementary context documents
examples/
cursor-mcp.json Example Cursor MCP config
claude-desktop-config.json Example Claude Desktop config
tests/
health-check.test.tsNon-goals (by design)
No first-party credential storage — it recommends secret managers / managed-auth brokers, never holds a secret
No auth / OAuth / accounts — the hosted endpoint is read-only and stateless, nothing to log into
No vector database
No graph database (Neo4j etc.)
No automatic registry updates
No LLM API calls inside MCP tools
No SaaS dashboard
No dependency on OrchestrateLab at runtime
Build order
MAR-35 ✅ Scaffold — done
MAR-37 ✅ Graph registry schemas: components, edges, stacks, routes, playbooks
MAR-38 ✅ Seed workflow graph baseline
MAR-77 ✅ Graph lookup tools: list/get components, edges, stacks, routes
MAR-78 ✅ compose_workflow_route — deterministic route composer
MAR-49 ✅ Benchmark setup — see docs/BENCHMARKING.md
MAR-88 ✅ Domain-gated capability matcher — eliminates cross-domain false positives
MAR-92 ✅ Registry lint + untested_edge_pct in health_check
MAR-95 ✅ crm_note_write component + research→content bridge edge
MAR-96 ✅ Benchmark protocol v2 — rubric, prompts-v2.yaml, PROTOCOL.md
MAR-97 ✅ Docs truth pass — registry counts, tool count, verify pathBenchmarking
To validate that the workflow graph improves planning quality over vanilla Cursor/Claude, run the manual benchmark described in docs/BENCHMARKING.md.
Quick start:
# v2 protocol — print session guide for all 7 prompts
pnpm tsx scripts/benchmark-template.ts --prompts benchmarks/prompts-v2.yaml --all
# v2 — single prompt
pnpm tsx scripts/benchmark-template.ts --prompts benchmarks/prompts-v2.yaml --prompt p6_email_lead_crm
# v1 (legacy)
pnpm tsx scripts/benchmark-template.tsResults go in benchmarks/results-YYYY-MM-DD.md.
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