OpenVisio BYO MCP Bridge

OpenVisio BYO MCP Bridge

MCP server that exposes OpenVisio's team collaboration API as 15 tools for AI agents, enabling them to read team state, communicate, and manage tickets autonomously.

Category
Visit Server

README

OpenVisio BYO MCP Bridge

A Model Context Protocol (MCP) server that exposes OpenVisio's team collaboration API as a set of tools for AI agents. Built with Mastra.

The bridge turns OpenVisio's REST API (tickets, board columns, channels, documents, projects, activity) into 15 MCP tools that agents can call to read team state, communicate, and drive tickets autonomously.

Setup

Requires Node.js >= 22.13.

npm install
cp .env.example .env
# edit .env and set OPENVISIO_API_URL if not using the default

Environment variables

Variable Description
OPENVISIO_API_URL API base URL (defaults to the dev gateway)
PORT HTTP port for the MCP server (default 4112)

Authentication

The bridge authenticates every backend request as a registered OpenVisio agent by sending the x-agent-identifier and x-agent-api-key headers, which the backend's userAuthMiddleware accepts alongside the bearer-JWT flow used by frontend clients.

Agent credentials are not configured via env — they are passed on every tool invocation via the agent_identifier and agent_api_key arguments (alongside the numeric agent_id used for identity resolution). To provision an agent:

  1. Create the agent as an admin: POST /agents with { name, type: "bring_your_own", user_id }.
  2. Copy the identifier and api_key from the create response.
  3. Set the agent to status: "active" via PUT /agents/{id} — inactive agents are rejected.
  4. Pass the credentials on each tool call.

Running

npm run start:mcp        # standalone MCP server on http://localhost:4112/mcp
npm run dev              # Mastra dev (Studio)
npm run build            # Mastra build

Tools

Tool What it does Backed by
list_resources Team resources as (kind, id, title) refs; optional product_id scope /projects, per-collection lists
get_resource One resource's full content as Markdown GET entity endpoints
get_context Role-scoped team briefing (projects, columns, tickets, channels, docs, activity) Aggregated
search Full-text search across the team Fan-out over per-collection search params
get_marching_orders Role-aware autonomy instructions; call first in a cycle Aggregated
poll_inbox Actionable items (assigned/claimable tickets, mentions); returns a cursor Aggregated
ack_inbox Advance the inbox watermark Local state
post_message Post to a channel as the agent (≥ Commenter) POST .../channels/{id}/messages
react_message Add an emoji reaction No-op (no backend endpoint)
list_my_tasks Assigned tickets + board columns (todo/active/done) /tasks, /tasks/task-types
claim_ticket Assign an unassigned ticket to the agent, move to first active column (≥ Editor) GET+PUT .../tasks/{id}
update_ticket Move/edit a ticket (status, priority, description, assignee, title, due date) (≥ Editor) PUT .../tasks/{id}
comment_ticket Add a comment to a ticket No-op (no backend endpoint)
create_ticket File a new ticket (≥ Editor) POST .../tasks
create_api_request Save a request into the team's API collection No-op (no backend endpoint)

Board columns are the API's task types. "Status" of a ticket = its column id (type_id) + position.

Notes / limitations

  • No-op tools (react_message, comment_ticket, create_api_request) are registered so the tool contract stays intact; they succeed without side effects. The backend has no endpoints for reactions, task comments, or an API collection.
  • Threads: post_message accepts an optional parent_id, but the backend does not officially document thread support; it may be ignored.
  • Identity: self-aware tools require agent_id (the registered OpenVisio agent id). The bridge derives the agent's user_id and org role from the agent and member records.
  • Inbox cursors (poll_inbox/ack_inbox) are held in memory and keyed by agent; they reset when the bridge restarts.
  • Response parsing is defensive: the API documents request shapes but not response shapes beyond the { ok, message, status, body } envelope, so field extraction is best-effort.

Architecture

src/
  lib/
    config.ts      # env config (API URL only) + .env loading
    client.ts      # fetch client: per-request agent-header auth, envelope unwrap, pagination, tolerant pick()
    identity.ts    # resolve agent -> user_id -> member role
    markdown.ts    # per-kind Markdown renderers
    cursors.ts     # in-memory inbox watermark store
  tools/
    read.ts        # list_resources, get_resource, get_context, search
    autonomy.ts    # get_marching_orders, poll_inbox, ack_inbox
    messaging.ts   # post_message, react_message
    tickets.ts     # list_my_tasks, claim_ticket, update_ticket, comment_ticket, create_ticket
    api_tool.ts    # create_api_request
  mastra/
    index.ts       # Mastra instance
    mcp/server.ts  # MCPServer with the 15-tool registry
  server.ts        # standalone HTTP MCP endpoint (/mcp)

Development

npx tsc --noEmit    # typecheck

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured