Workato Dev MCP

A local, zero-dependency MCP server to author, lint, deploy, and debug Workato recipes from Claude (Code, Desktop, or any MCP client). It wraps the Workato Developer REST API, the Recipe Lifecycle Management (RLCM) package API, and the Data Tables record API.

Why not the official one? The hosted Workato Developer MCP (app.workato.com/mcp) is management/read-only — it can't create or update recipe code or start/stop recipes. This can. It also adds a pure-Python recipe linter, RLCM export/import, Data Table row CRUD, and AI Hub MCP-server management.

• Zero dependencies — Python 3.8+ standard library only. No pip install, no build step.

• Bring your own token — each developer uses their own Workato API-client token. Nothing hosted or shared.

• 56 tools across recipes, linting, RLCM round-trip, connections, data/lookup tables, API Platform, and AI Hub MCP servers.

Forked from krishnagutta/workato-dev-mcp; extends it with RLCM round-trip, Data Table CRUD, a scaffolder, AI Hub MCP management, and a pure-Python port of Workato Labs' recipe-lint (MIT). Full attribution in LICENSE.

Setup (Claude Code)

First get a Workato API-client token: Workspace admin → API clients (which privileges to grant — and how to wire Claude Desktop / other clients — are in docs/SETUP.md). Then:

git clone https://github.com/gmunro-lyft/Workato-Claude-MCP.git
cd Workato-Claude-MCP
cp .env.example .env                 # 1. copy the env template
# 2. edit .env → set WORKATO_TOKEN=wrkaus-…
python3 mcp/server.py --selftest     # 3. confirm the server loads (no token needed)
claude                               # 4. launch Claude Code; approve "workato-dev" once

Then in a new session try: "list my Workato recipes" or "lint recipe 12345678".

Related MCP server: salesforce-mcp

The recipe edit loop

get_recipe(id, include_code=true)   # pull the code tree (source of truth for editing)
  → edit the JSON in conversation
  → lint_recipe(recipe_id=id)       # catch structural/datapill bugs BEFORE saving
  → stop_recipe(id)                 # running recipes can't be updated
  → update_recipe(id, code=<json>)
  → start_recipe(id)                # start = the real compiler; read its validation errors
  → list_jobs / get_job             # after a test run, inspect real per-step I/O

Run workato_recipe_tips and get_learnings once before building — they capture the datapill format, the trigger extended_output_schema requirement, the HTTP string-body trick, custom-code schema gotchas, valid condition operands, and the job-log debugging pattern.

Linting

Two local, no-token tools statically analyse recipes before deploy: lint_recipe (one recipe, by path or recipe_id) and validate_package (a whole package directory + package-level checks). Both return structured diagnostics from a pure-Python port of recipe-lint's Tier 0/1 rules. See docs/LINTING.md for the rule table and the full 66-rule engine.

Region

Defaults to the US data center. For another region set WORKATO_API_BASE (and WORKATO_DATA_TABLES_BASE for Data Table row tools) in .env — e.g. EU is https://app.eu.workato.com/api. Full table in docs/SETUP.md.

Verify & test

All of these are offline, need no token, and require no install (stdlib only):

python3 mcp/server.py --selftest          # the tool registry loads and is internally consistent
python3 mcp/server.py --list-tools        # print every tool + a one-line description
python3 mcp/server.py --version           # print the server version
python3 -m unittest discover -s tests     # run the unit tests
python3 tools/gen_tools_doc.py --check    # docs/TOOLS.md is in sync with the tools

The tests also run under pytest if you prefer it.

Documentation

• docs/SETUP.md — create the API client, privileges, token wiring (Code / Desktop / other), regions

• docs/TOOLS.md — the full 56-tool reference (generated from the server)

• docs/LINTING.md — lint rules + the upstream Go engine

• CONTRIBUTING.md — add a tool, run the checks, and the allowlist-.gitignore rule

Capturing learnings

The knowledge base grows as you use it. mcp/learnings.md is the append-only intake queue — when Claude hits a gotcha not in workato_recipe_tips, it calls log_learning. Scrub any workspace-specific names/IDs before committing (see CONTRIBUTING) — the file is tracked and publishes verbatim. During review, raw learnings are promoted into workato_recipe_tips (the curated tier). log_learning / get_learnings are pure local file ops — no token.

Notes / limits

• This is a dev tool. It can create, edit, start/stop, and delete recipes and Data Table rows. Point it at a dev/impl workspace and treat delete_recipe / deploy_package / delete_data_table_row with care (they confirm intent in their descriptions).

• AI Hub MCP tools may be plan-gated. The *_mcp_* tools need the API client's MCP privilege; without it Workato returns the HTML login page or 403/404. Run check_mcp_privileges first. (A different surface from the API-Platform list_api_* tools.)

• It doesn't touch the AI Hub authoring layer beyond server management. After a recipe param-set change, the consuming MCP client needs an app restart to see the new schema.

• Sharing. Commit this folder to an internal git repo; teammates clone, set WORKATO_TOKEN in .env, and approve the project-scoped .mcp.json on first launch.

License

MIT. Builds on krishnagutta/workato-dev-mcp and ports lint rules from recipe-lint (MIT) — see LICENSE.