project-mcp
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., "@project-mcp Create a new FastAPI project with the default template"
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.
Project Development MCP Server
A FastMCP server that exposes predefined artifacts (templates, configs, code snippets, assets) as Resources and project lifecycle operations as Tools. Use it from Cursor or any MCP client to create, update, deploy, debug, test, monitor, and configure projects with minimal token usage.
Recommended: HTTP transport. Run the server once; all clients (Cursor, other IDEs, CLIs) connect to the same URL. One process, shared use, no per-client spawn.
Setup
Option A: Nix + devenv (recommended)
With Nix and devenv installed:
cd project-mcp
devenv upThis installs Python and uv, runs uv sync, and starts the MCP server on HTTP at http://localhost:8000/mcp. Leave it running; point Cursor and other clients at that URL. Use direnv allow if you use direnv (optional for devenv up).
Option B: uv only
cd project-mcp
uv syncRelated MCP server: my-mcp-server
Running the server
HTTP (recommended) — one server for all clients:
# With devenv (starts HTTP server)
devenv up
# Or with uv only (HTTP is the default)
uv run python server.pyServer base URL: http://localhost:8000/mcp (port 8000 unless you set MCP_PORT). Connect Cursor and other clients to this URL; no need for each client to run the server. When using HTTP, a /health endpoint returns {"status": "ok"} for load balancers or k8s probes.
Stdio (alternative) — Cursor or another client runs the server as a subprocess (one process per client). Set MCP_TRANSPORT=stdio or use the fastmcp CLI:
MCP_TRANSPORT=stdio uv run python server.py
# or
uv run fastmcp run fastmcp.jsonUse stdio if you prefer zero “run the server” step and only one client.
Configuration
PROJECT_MCP_ROOT— Root directory for project paths (default: current working directory). All tool paths (target_path,project_path,path) must resolve under this root; path traversal (e.g.../) is rejected. Set this to your workspace or a dedicated projects directory to scope and secure where the server can read/write. At startup, the server warns if this is set but not a directory or missing.PROJECT_MCP_ALLOWED_COMMANDS— Comma-separated list of command prefixes allowed byrun_command(e.g.python,npm,uv). If unset, defaults to: python, npm, npx, uv, pip, node, pytest, make.MCP_TRANSPORT—http(default) orstdio.MCP_PORT— Port for HTTP (default:8000).LOG_LEVEL— Logging level (default:INFO). Set toDEBUGfor more verbose tool logs.
Running tests
Install dev dependencies (pytest, ruff), then run the test suite:
uv sync --extra dev
uv run pytest tests/ -vTo run linting and format checks (same as CI):
uv run ruff check .
uv run ruff format --check .Cursor integration
HTTP (recommended): Run the server once (e.g. devenv up or the HTTP command above), then add the server in Cursor by URL. Example MCP config (e.g. in Cursor Settings → MCP or .cursor/mcp.json):
{
"mcpServers": {
"project-dev": {
"url": "http://localhost:8000/mcp"
}
}
}If your Cursor version uses a different shape (e.g. transport: "sse" with a separate url), see Cursor + FastMCP. Use your actual host/port if not localhost. All Cursor windows and other clients can use the same running server.
Stdio (alternative): Cursor runs the server itself. In MCP settings use a command instead of a URL:
{
"mcpServers": {
"project-dev": {
"command": "uv",
"args": ["run", "fastmcp", "run", "fastmcp.json"],
"cwd": "/absolute/path/to/project-mcp"
}
}
}Replace /absolute/path/to/project-mcp with the real path.
Artifacts and URI scheme
Predefined content is organized by context first (folder under artifacts/), then type (folder under each context). Context is a flexible grouping—maintainers choose the strategy that fits their needs (e.g. by technology, project type, or other axes).
URI pattern: artifact://{context}/{type}/{path}
Part | Purpose | Examples |
| Grouping chosen by maintainer |
|
| Kind of artifact under that context |
|
| Relative path under context/type |
|
Context examples:
By technology:
fastapi,react,aws,gcpBy project type:
internal-admin,data-pipeline,research-notebook,app-documentationdefault: generic, stack-agnostic artifacts only
URI examples:
artifact://default/configs/pyproject.toml— generic Python configartifact://default/snippets/hello.py— generic hello snippetartifact://fastapi/templates/fastapi-app— FastAPI app templateartifact://react/templates/react-component.tsx— React component templateartifact://data-pipeline/configs/dag.yaml— data pipeline DAG config
Add new contexts by adding a folder under artifacts/; add new types by adding a folder under a context. No server code changes required. Use Resources to read these URIs on demand so the LLM does not hold large blobs in context.
Tools
Tool | Description |
| List available artifacts (optionally filter by context/type). Returns JSON with |
| Create project from a template; use |
| Read a file at path (under project root). |
| List directory contents at path (one level). |
| Search for regex pattern in project files; optional include/exclude globs. |
| Replace old_string with new_string in file (first or all). |
| Write or overwrite a file under the project root. |
| Run tests (pytest or npm test). |
| Run deploy (Makefile, npm run deploy, or custom script). |
| Run an allowed command in project dir (python, npm, uv, etc.). |
| Project status and detected type. |
| Recent log content from |
| Read config key (e.g. name, version) from pyproject/package.json. |
| Update name or version in pyproject.toml or package.json. |
All paths are validated against PROJECT_MCP_ROOT to prevent path traversal.
Usage examples
From an MCP client (e.g. Cursor), you can call tools and read resources like this:
Discover artifacts: Call list_artifacts() (or list_artifacts(context="fastapi")) to get a JSON list of artifact URIs, then read any via the Resource artifact://{context}/{type}/{path}.
Create a FastAPI project:
create_project(template_id="fastapi-app", target_path="./my-api", context="fastapi")Create a project with template variables: If the template contains {{project_name}} or {{version}}, pass them in:
create_project(template_id="var-test", target_path="./my-app", context="default", variables={"project_name": "MyApp", "version": "1.0"})Write a file: write_file(path="src/main.py", content="print('hello')")
Project status: status(project_path=".") — returns detected type (Python/Node) and top-level listing.
Run tests: run_tests(project_path=".") — runs pytest or npm test based on project type.
Project layout
project-mcp/
├── server.py # FastMCP app and registration
├── path_util.py # Path validation helpers
├── artifact_loader.py # Artifact discovery and read (type/context/path)
├── fastmcp.json # FastMCP project config
├── devenv.nix # Nix + devenv (packages, process)
├── devenv.yaml # Devenv inputs
├── .envrc # direnv: use devenv
├── pyproject.toml
└── artifacts/ # Client-facing content: artifact://{context}/{type}/{path}
├── default/ # generic, stack-agnostic only
│ ├── configs/ # pyproject.toml, tsconfig.json, Dockerfile
│ ├── snippets/ # hello.py
│ └── assets/ # placeholder.svg
├── fastapi/ # context: technology
│ └── templates/ # fastapi-app
├── react/ # context: technology
│ └── templates/ # react-component.tsx
# Add contexts as needed: internal-admin/, data-pipeline/, aws/, gcp/, etc.License
MIT.
Available Tools
14 toolsdeployCDestructive
Trigger deployment for a project (runs deploy script or make deploy).
Inputs: target (required), project_path (required), options. Returns: result
edit_fileBDestructive
Replace old_string with new_string in file. replace_all: all (default) or first only.
Inputs: path (required), new_string (required), old_string (required), replace_all. Returns: result
get_configBRead-only
Read a config value from project (e.g. pyproject name, package.json name).
Inputs: key (required), project_path (required). Returns: result
get_logsBRead-only
Read recent log content from project (looks for log files or .log).
Inputs: project_path (required), lines, source. Returns: result
list_artifactsBRead-only
List artifacts; filter by context/type. Returns JSON with uri per artifact.
Inputs: type, context. Returns: result
list_directoryARead-only
List directory contents at path (relative to PROJECT_MCP_ROOT or cwd). One level only.
Inputs: path. Returns: result
read_fileARead-only
Read a file at path (relative to PROJECT_MCP_ROOT or cwd). Returns content or error.
Inputs: path (required). Returns: result
run_commandC
Run a command in project dir. Allowed prefixes: PROJECT_MCP_ALLOWED_COMMANDS or default.
Inputs: command (required), project_path (required), env. Returns: result
run_testsC
Run tests in project (pytest for Python, npm test for Node).
Inputs: project_path (required), scope, extra_args. Returns: result
search_filesBRead-only
Search for regex in project files. include/exclude globs. Returns path:line_num: line.
Inputs: pattern (required), project_path (required), exclude, include, +1 more. Returns: result
statusBRead-only
Return project status: detected type, key files, and recent dir listing.
Inputs: project_path (required). Returns: result
_substitute_varsADestructive
Replace {{key}} with variables[key] in text. Leaves {{key}} if key missing.
Inputs: text (required), variables (required). Returns: result
update_configCDestructive
Update name or version in pyproject.toml or package.json.
Inputs: key (required), value (required), project_path (required). Returns: result
write_fileBDestructive
Write or overwrite a file at path (relative to PROJECT_MCP_ROOT or cwd).
Inputs: path (required), content (required). Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 14 tools
Several tools overlap in purpose: run_command can duplicate run_tests and deploy, read_file/get_config both read config files, edit_file/update_config can both modify config, and list_artifacts/list_directory/search_files share discovery space. Descriptions help, but an agent could plausibly misselect among these.
Most tools follow a readable snake_case verb_noun pattern (read_file, write_file, get_config, update_config, run_tests). Minor deviations include _substitute_vars with a leading underscore, plus deploy and status as bare verb/noun names.
14 tools is within the typical well-scoped range and matches a project lifecycle surface spanning files, config, tests, deployment, commands, status, and logs. No excessive proliferation or obvious under-provisioning.
Core project operations are covered: file read/write/list/search/edit, config get/update, test running, deployment, command execution, status, and logs. Minor gaps remain around explicit delete_file/create_directory and artifact lifecycle beyond listing, though run_command can work around many of them.
Maintenance
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