accelo-mcp

An unofficial Model Context Protocol server for the Accelo CRM platform. It exposes Accelo data and operations to MCP-compatible AI assistants through the documented Accelo REST API.

This project is not affiliated with or endorsed by Accelo. Users are responsible for their Accelo account, OAuth application, permissions, and data handling.

Installation

With uvx

The recommended installation for MCP hosts is:

{
  "mcpServers": {
    "accelo": {
      "command": "uvx",
      "args": ["accelo-mcp"],
      "env": {
        "ACCELO_DEPLOYMENT": "your-deployment",
        "ACCELO_CLIENT_ID": "your-client-id",
        "ACCELO_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

To pin a release:

"args": ["accelo-mcp==0.1.0"]

uvx installs the package into an isolated environment and runs the accelo-mcp command.

With pip

python3 -m pip install accelo-mcp
accelo-mcp

From source

git clone <repository-url>
cd accelo-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Configuration

Copy .env.example to .env, or provide these variables through the MCP host:

HTTP transport variables (only used with --http or --sse):

Create or obtain an OAuth application for the Accelo deployment you intend to access. Never commit client secrets, refresh tokens, access tokens, cookies, or webhook secrets.

Authentication types

All four authentication types use OAuth 2.0. They differ in whether a person authorises interactively in a browser and whether a client secret is required.

Key points:

• public still requires OAuth. "Public" means no client secret is distributed, not that authentication is skipped. On first run the server opens your browser, you log in to Accelo and authorise, Accelo redirects back to a local 127.0.0.1 callback listener, and the server exchanges the authorisation code (using a PKCE code_verifier) for tokens. No client secret is ever sent.
• service is the only non-interactive flow. It authenticates as a service identity via the Client Credentials grant and never opens a browser. Every other type requires a one-time interactive authorisation.
• After the first authorisation, web, public, and installed cache a refresh token and reauthorise silently, so the browser step is not repeated on later runs.
• The redirect URI must line up in three places for web and public: the ACCELO_REDIRECT_URI you set, the port the local callback listener binds (derived from that URI, or 8080 if the URI has no explicit port), and the redirect URI registered on the Accelo application. Accelo rejects the authorisation request if its registered value does not match. For public, ACCELO_REDIRECT_URI is mandatory and has no default.

Tokens are cached locally at ~/.accelo-mcp/tokens.json with restrictive file permissions. The local callback listener binds to 127.0.0.1 only.

Usage

The default transport is stdio, which is suitable for Claude Desktop, Kiro, and other local MCP hosts:

accelo-mcp

For remote or networked hosts, run the server over Streamable HTTP (the current MCP HTTP transport):

accelo-mcp --http

This listens on http://127.0.0.1:3000/mcp by default. To serve from inside a container or behind a reverse proxy or load balancer, bind all interfaces and allow-list the public hostname so DNS-rebinding protection does not reject proxied requests:

ACCELO_MCP_HOST=0.0.0.0 \
ACCELO_MCP_PORT=8080 \
ACCELO_MCP_ALLOWED_HOSTS=mcp.example.com \
ACCELO_MCP_ALLOWED_ORIGINS=https://mcp.example.com \
accelo-mcp --http

Set ACCELO_MCP_STATELESS=true for horizontally scaled deployments where any instance may serve any request.

The older SSE transport is still available but deprecated; prefer --http for new deployments:

accelo-mcp --sse

The server provides 219 tools covering companies, contacts, affiliations, activities, projects, tickets, tasks, retainers, sales, billing, time tracking, custom fields, resources, workflow progressions, and related Accelo objects.

For complex queries, call accelo_get_context first. It describes the Accelo object model, relationships, filtering conventions, pagination, and field selection syntax.

Filtering and field selection

All list tools accept structured filters:

{"standing": "active"}
{"date_created_after": 1690000000}
{"status": [1, 2, 3]}
{"order_by_desc": "date_modified"}

Request linked or optional fields with fields:

website,phone
postal_address(city,state)
_ALL

Development

make install  # install the package and development dependencies
make test     # run tests
make lint     # run Ruff checks
make audit    # audit Python dependencies for known vulnerabilities
make check    # run lint, tests, and dependency audit
make build    # build a wheel and source distribution

Releasing

Releases are driven by the version in pyproject.toml. Bumping that version on main is the only manual step; validation, tagging, the GitHub Release, and the PyPI publish are automated by a single workflow.

1. Bump version in pyproject.toml (for example 0.1.0 to 0.2.0) on a branch and merge it to main through a pull request. Follow semantic versioning.

2. On merge, the Release workflow (.github/workflows/publish.yml) detects the changed version and, in one run:

   • runs make check (lint, tests, and dependency audit),
   • creates the matching v<version> tag and a GitHub Release with generated notes, and
   • builds the wheel and source distribution and publishes them to PyPI via OIDC Trusted Publishing (no stored API token).

   The workflow is idempotent: if the version is unchanged since the previous commit, or the tag already exists on the remote, it does nothing.

Tagging and publishing live in the same workflow on purpose. A tag pushed by the built-in GITHUB_TOKEN does not trigger a second workflow, so a separate tag-triggered publish job would never run. Folding both into one push-triggered workflow avoids that loop-guard.

To cut a release by hand, bump the version and push the tag yourself:

git tag v0.2.0   # must equal "v" + the pyproject.toml version
git push origin v0.2.0

Note that a hand-pushed tag alone does not publish (nothing is triggered by a tag push); publishing happens through the version bump on main. The Release workflow still verifies the tag equals v + the pyproject.toml version and fails if they differ, so the tag and the package version cannot drift.

Publishing to PyPI requires a configured pypi GitHub environment and a PyPI Trusted Publisher registered for this repository's publish.yml workflow. The workflow file must stay named publish.yml: PyPI matches the trusted publisher on the exact workflow filename, so renaming it breaks publishing even though the workflow's display name is "Release".

Security

Please report suspected vulnerabilities privately. See SECURITY.md for the reporting process. Do not include credentials, customer data, or access tokens in an issue.

License

MIT. See LICENSE.