MCPSystem

MCPSystem

A local runtime for persistent, isolated replicas of services such as GitHub, GitLab, Bitbucket, Jira, Linear, and YouTrack, providing MCP surfaces for agents to interact with software-company resources.

Category
Visit Server

README

MCPSystem

MCPSystem is a local runtime for persistent, isolated replicas of services such as GitHub, GitLab, Bitbucket, Jira, Linear, and YouTrack. External services are sources of API contracts and optional conformance checks; they are not runtime dependencies.

The MCP service foundation milestone is complete. It provides:

  • a versioned service-plugin contract;
  • a persistent control plane;
  • isolated per-environment service databases;
  • transactional plugin migrations and seeding;
  • persisted selection of MCP surfaces;
  • strict TOML environment/template configuration;
  • immutable templates and isolated clone-on-create environments;
  • immutable point-in-time environment snapshots, independent snapshot clones, and structured SQL/Git snapshot diffs;
  • restart and isolation guarantees covered by tests.
  • a durable, transport-neutral provider operation log.

Task generation, benchmark scenarios, perturbation-time ground truth, filtering, and the Oracle are the next benchmark-harness layer; they are intentionally outside the completed MCP service foundation.

Built-in service plugins

The built-in github@0.1.0 plugin provides SQLite and PostgreSQL schemas for the core software-company resources and a deterministic minimal bootstrap. Its first transactional operation set covers repositories, issues, labels, assignees, comments, relational commit/branch state, pull requests, requested reviewers, reviews, review comments, and merge transitions. Each environment also owns isolated local bare-Git repositories containing the real blobs, trees, commits, and refs. Contract provenance and the current coverage boundary are documented in docs/services/github.md.

The bounded gitlab@0.1.0 core adds groups/projects, labels, issues/notes, repository files/commits/branches/tags, merge requests/discussions/approvals, pipelines/jobs/statuses, and releases. It preserves the same SQLite/PostgreSQL isolation and real-Git guarantees and exposes 78 MCP tools through gitlab_rest_v4 plus 78 matching HTTP routes under /api/v4.

The bounded jira@0.1.0 core adds users, projects, issues, comments, workflow transitions, issue links, Scrum boards, and sprint lifecycle. Its jira_rest_v3 MCP surface exposes 22 tools backed by isolated relational state.

The six built-in surfaces expose 222 tools in the combined company template through a stateful MCP 2025-11-25 JSON-RPC stdio server. Environment, actor, and service routing are fixed when the server starts rather than accepted from model-controlled tool arguments. Setup and protocol details are in docs/mcp.md.

Agents connect directly to these local, contract-compatible MCP surfaces. The GitHub and GitLab REST implementations remain useful for conformance testing, but no vendor MCP process or external service is part of the runtime.

For a combined six-service environment and role-bound client config, run scripts/materialize_company.py followed by scripts/generate_mcp_config.py. Exact commands are in docs/mcp.md.

MCP and HTTP provider calls are recorded in the same persistent operation timeline for future task inspection and standup/release artifacts. See docs/operation-log.md.

The completed boundary is deliberately bounded: local agents use MCP over stdio, and repository work uses explicit provider-shaped commit/file/branch operations backed by real bare Git. Streamable HTTP MCP, Git smart protocol, working-tree checkout, and complete vendor-wide API parity are excluded until a benchmark workflow requires them.

Inspect environments and their MCP/HTTP operation timeline in the local read-only UI:

PYTHONPATH=src .venv/bin/python scripts/inspector.py --data-root data --port 8777

The Inspector projects all six providers into the same author-facing model: repositories/projects, tickets/issues, pull/merge requests, reviews/approvals, real Git diffs, Actions/pipelines, and Jira project tickets. Built-in templates live under configs/templates/.

Then open http://127.0.0.1:8777. The UI and its loopback-only security boundary are documented in docs/inspector.md. Its Artifacts workbench uses provider-neutral ticket/change-set/review/build projections rather than copying the GitHub interface.

Materialize the GitHub template and one isolated PostgreSQL environment:

MCP_SYSTEM_POSTGRES_DSN=postgresql://mcp_system:mcp_system@127.0.0.1:55432/mcp_system \
  .venv/bin/python scripts/materialize_github.py

Run its MCP server after substituting the printed environment id:

.venv/bin/python scripts/mcp_server.py \
  --environment ENVIRONMENT_ID \
  --actor engineer \
  --postgres-dsn postgresql://mcp_system:mcp_system@127.0.0.1:55432/mcp_system

Run tests

PYTHONPATH=src .venv/bin/python -m unittest discover -s tests -v

PostgreSQL backend

Start the local PostgreSQL 18 instance:

docker compose up -d --wait postgres

Construct the runtime with persisted PostgreSQL control and service schemas:

from pathlib import Path
from mcp_system import MCPSystem, PluginRegistry

registry = PluginRegistry()
# Register service plugins before opening or creating environments.

system = MCPSystem.with_postgres(
    Path("data"),
    registry,
    "postgresql://mcp_system:mcp_system@127.0.0.1:55432/mcp_system",
)

Run PostgreSQL integration tests:

MCP_SYSTEM_TEST_POSTGRES_DSN=postgresql://mcp_system:mcp_system@127.0.0.1:55432/mcp_system \
  .venv/bin/python -m unittest discover -s tests -v

Declarative environment

[environment]
name = "local software company"
mcp_surfaces = ["github_standard", "codebase"]

[[services]]
instance_id = "code_host"
plugin = "github"
version = "1.0.0"

[services.seed]
organization = "acme"

Template configuration uses the same services array with a [template] header containing id, name, version, and mcp_surfaces.

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