MCPSystem
Provides a local, persistent replica of GitHub with MCP tools for managing repositories, issues, labels, assignees, comments, commit/branch state, pull requests, requested reviewers, reviews, review comments, and merge transitions.
Provides a local, persistent replica of GitLab with MCP tools for managing groups/projects, labels, issues/notes, repository files/commits/branches/tags, merge requests/discussions/approvals, pipelines/jobs/statuses, and releases.
Provides a local, persistent replica of Jira with MCP tools for managing users, projects, issues, comments, workflow transitions, issue links, Scrum boards, and sprint lifecycle.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCPSystemlist open pull requests and issues in the GitHub environment"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 agent-facing
MCP tools through gitlab_rest_v4 plus 78 HTTP routes under /api/v4. The
CI-verdict writer is admin-only so an agent cannot forge a green build.
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 221 agent-facing 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. Every entry
carries a monotonic per-environment seq, so a caller can ask "what has this
actor done since I last looked". See docs/operation-log.md.
An external harness drives all of this in process: it creates and deletes
environments, freezes one for the duration of a snapshot, and exports a
byte-stable image of a whole world. Those primitives and the reasoning behind
each are in docs/harness-integration.md; the two-service environment they
use is configs/templates/aabench-gitlab-jira.toml.
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 8777The 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.pyRun 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_systemRelated MCP server: CodexPro Runtime
Run tests
PYTHONPATH=src .venv/bin/python -m unittest discover -s tests -vPostgreSQL backend
Start the local PostgreSQL 18 instance:
docker compose up -d --wait postgresConstruct 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 -vDeclarative 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.
This server cannot be deployed
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