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Glama

Edworking MCP

Edworking MCP on Smithery Edworking MCP on Glama

The official, open-source Edworking MCP server. Connect your assistant to projects, tasks, Docs, conversations and files your account can access.

Hosted endpoint: https://mcp.edworking.com/mcp

Public identity: Edworking MCP. The Railway infrastructure retains the codename Bellsprout. Use the custom domain above for all new connections. Connections created against the earlier Railway URL must reconnect because their OAuth resource audience has changed.

Release: This source prepares 0.2.0 with 22 tools. Deploy the matching scoped API backend before the MCP server. Docs require new docs:read / docs:write token permissions and OAuth consent. Existing grants are not expanded automatically.

Published baseline: 0.1.4 is available through npm, the Official MCP Registry, Smithery and the Glama hosted connector. OpenAI review was submitted on 13 October 2026 and is pending; it is not yet published in OpenAI. See the publication checklist for individual directory statuses.

Connect

Add the endpoint to an MCP client supporting Streamable HTTP and OAuth. Follow the browser authorization window. Create a dedicated scoped token in Edworking → Profile → API tokens, enter it on the Edworking MCP consent page and approve the client and permissions. Never paste a token into an assistant conversation.

Connections request read permissions by default. Writes require the matching OAuth scope and upstream token permission. Reconnect with explicit scopes if your client cannot request additional permissions. Revoke the dedicated token in Edworking to disconnect every grant using it. Grants expire within 30 days or when the Edworking token expires, whichever is sooner. Access tokens last up to ten minutes and refresh tokens rotate.

codex mcp add edworking-mcp --url https://mcp.edworking.com/mcp
codex mcp login edworking-mcp

For ChatGPT testing, use Plugins → Add custom MCP server, select OAuth and enter the endpoint. Availability depends on your plan and workspace policy. A portable OpenAI plugin package is in plugin/. The 0.2.0 package declares six read and five write OAuth scopes, including Docs. The already-submitted OpenAI review package remains a separate 0.1.4 artifact until a new version is submitted. Verify the actual consent screen and get_connection result. On 13 October, the submission portal metadata grant used five read scopes, while a separate ChatGPT demo connection received all nine scopes and successfully created a task and sent a message. Existing read-only grants remain read-only until reauthorized; a token with write permissions does not automatically upgrade an OAuth grant.

Other clients should configure a remote HTTP server named edworking-mcp and use their built-in OAuth flow. Public clients with PKCE, dynamic registration and HTTPS Client ID Metadata Documents are supported. /mcp accepts only Edworking MCP access tokens.

Client setup instructions: Edworking MCP guide. See the competitor review and delivery roadmap for remaining capabilities and launch gates.

After Allow access or Cancel, the browser returns to your assistant automatically. If it remains on Returning to your assistant, use the return button. Each consent request can be used once and expires after ten minutes. If you see invalid_grant or an expired/used request, start a new connection from your assistant instead of reopening the old consent link. Do not refresh or resubmit the approval form.

Related MCP server: Todocko MCP Server

Tools

Tool

Scope

Purpose

get_connection

Any connection

Connection name, permissions and expiry

list_projects, list_project_statuses, list_project_members

projects:read

Projects, valid statuses and existing assignee IDs

list_tasks, get_task

tasks:read

Filter tasks by project, status, assignee, priority, title and due dates; read task content

search, fetch

Per-source read scope

Indexed tasks, Docs, files and project messages; typed result retrieval

list_docs, get_doc

docs:read

Find Docs and read their Markdown content

create_doc, update_doc, edit_doc_content

docs:write; body edits also need docs:read

Create, rename and edit Docs

list_conversations

chats:read

Existing private conversations

read_messages

messages:read

Accessible conversation messages

list_files

files:read

File metadata

create_task, update_task, edit_task_content

tasks:write; body edits also need tasks:read

Create tasks; edit name, status, priority, assignees, dates and description

send_message

messages:write

Send the user's specified message

create_project

projects:write

Create a project

upload_file

files:write

Import a public HTTPS file

Attaching files to tasks also requires files:write. Lists use cursors and at most 100 items per page; keep ordering and filters unchanged while paging. Search uses the workspace index and returns up to 20 ranked results per permitted source. It rechecks live access, deletion state and message-history limits before exposing snippets. Indexing may lag; unavailable sources are reported, and private chats and projectless items are excluded. Use filtered lists for exhaustive task discovery. File fetches return metadata, not file contents.

Read a task or Doc before editing its body. Content reads are capped at 20,000 characters and expose block handles; targeted edits reuse the collaboration service and version history. Large removals require explicit allowRemoval. A task assignee list replaces the current list: omit it to preserve, or use [] to unassign. Assignees must already belong to the project and workspace. Deletion, invitations, task moves and sharing changes are not exposed.

Writes require a UUID requestId; reuse it with unchanged arguments on retries. Completed results are encrypted and cached for 24 hours. An uncertain result is never repeated automatically: inspect Edworking before deciding what to do. This is not an upstream exactly-once transaction. Deduplication expires after 24 hours.

Development

Use Node 24 LTS (minimum 22.19). No OpenAI API key is needed.

npm ci
cp .env.example .env
# Generate a key and save it as TOKEN_ENCRYPTION_KEY in .env:
node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"
npm run build
node --env-file=.env dist/index.js

Run npm run check for type checking, security/integration tests and build. npm run dev starts a watcher when variables are already loaded by your shell.

For local stdio, configure your client to run npx -y @edworking/mcp@0.1.4 with EDWORKING_API_TOKEN supplied securely in its process environment, or build from source and run node /absolute/path/to/edworking-mcp/dist/stdio.js. The npm package is public. Stdio uses that token directly with Edworking and has in-memory deduplication that resets on restart.

Railway deployment

Use the included Dockerfile, attach a volume at /data, and set:

Variable

Value

NODE_ENV

production

PORT

3000

PUBLIC_BASE_URL

Stable public HTTPS origin, without /mcp

DATABASE_PATH

/data/bellsprout.sqlite

TOKEN_ENCRYPTION_KEY

Random 32-byte key encoded as 64 hex characters

EDWORKING_API_URL

https://gateway.edworking.com/

EDWORKING_APP_URL

https://app.edworking.com

Keep one replica, sleeping off and deployment overlap zero. /healthz checks the process and /readyz checks SQLite. Preserve the key across deployments. Optional ALLOWED_ORIGINS adds exact browser origins, OPENAI_DOMAIN_CHALLENGE serves the OpenAI verification value, and EDWORKING_CONNECT_URL enables a native Edworking connection page only after that page has shipped.

See operations, security and publication. Contributions follow CONTRIBUTING.md. Licensed under MIT.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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