Skip to main content
Glama

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

With Nix and devenv installed:

cd project-mcp
devenv up

This 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 sync

Related 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.py

Server 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.json

Use 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 by run_command (e.g. python,npm,uv). If unset, defaults to: python, npm, npx, uv, pip, node, pytest, make.

  • MCP_TRANSPORT — http (default) or stdio.

  • MCP_PORT — Port for HTTP (default: 8000).

  • LOG_LEVEL — Logging level (default: INFO). Set to DEBUG for more verbose tool logs.

Running tests

Install dev dependencies (pytest, ruff), then run the test suite:

uv sync --extra dev
uv run pytest tests/ -v

To 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

context

Grouping chosen by maintainer

default (generic), fastapi, react, internal-admin, data-pipeline

type

Kind of artifact under that context

templates, configs, snippets, assets, components, iac

path

Relative path under context/type

fastapi-app, pyproject.toml, Button.tsx

Context examples:

  • By technology: fastapi, react, aws, gcp

  • By project type: internal-admin, data-pipeline, research-notebook, app-documentation

  • default: generic, stack-agnostic artifacts only

URI examples:

  • artifact://default/configs/pyproject.toml — generic Python config

  • artifact://default/snippets/hello.py — generic hello snippet

  • artifact://fastapi/templates/fastapi-app — FastAPI app template

  • artifact://react/templates/react-component.tsx — React component template

  • artifact://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_artifacts

List available artifacts (optionally filter by context/type). Returns JSON with uri per artifact.

create_project

Create project from a template; use context to pick the group. Optional variables for {{key}} substitution.

read_file

Read a file at path (under project root).

list_directory

List directory contents at path (one level).

search_files

Search for regex pattern in project files; optional include/exclude globs.

edit_file

Replace old_string with new_string in file (first or all).

write_file

Write or overwrite a file under the project root.

run_tests

Run tests (pytest or npm test).

deploy

Run deploy (Makefile, npm run deploy, or custom script).

run_command

Run an allowed command in project dir (python, npm, uv, etc.).

status

Project status and detected type.

get_logs

Recent log content from .log files.

get_config

Read config key (e.g. name, version) from pyproject/package.json.

update_config

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 tools
deployC
Destructive

Trigger deployment for a project (runs deploy script or make deploy).

Inputs: target (required), project_path (required), options. Returns: result

edit_fileB
Destructive

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_configB
Read-only

Read a config value from project (e.g. pyproject name, package.json name).

Inputs: key (required), project_path (required). Returns: result

get_logsB
Read-only

Read recent log content from project (looks for log files or .log).

Inputs: project_path (required), lines, source. Returns: result

list_artifactsB
Read-only

List artifacts; filter by context/type. Returns JSON with uri per artifact.

Inputs: type, context. Returns: result

list_directoryA
Read-only

List directory contents at path (relative to PROJECT_MCP_ROOT or cwd). One level only.

Inputs: path. Returns: result

read_fileA
Read-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_filesB
Read-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

statusB
Read-only

Return project status: detected type, key files, and recent dir listing.

Inputs: project_path (required). Returns: result

_substitute_varsA
Destructive

Replace {{key}} with variables[key] in text. Leaves {{key}} if key missing.

Inputs: text (required), variables (required). Returns: result

update_configC
Destructive

Update name or version in pyproject.toml or package.json.

Inputs: key (required), value (required), project_path (required). Returns: result

write_fileB
Destructive

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

    B3.3/5.0

    Scored across 14 tools

    Disambiguation3/5

    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.

    Naming Consistency4/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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

    ActivityInactive
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      FastMCP is a comprehensive MCP server allowing secure and standardized data and functionality exposure to LLM applications, offering resources, tools, and prompt management for efficient LLM interactions.
      3
      MIT
    • F
      license
      B
      quality
      D
      maintenance
      A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.
      1
      -
    • F
      license
      Not graded
      quality
      Not graded
      maintenance
      A FastMCP server that exposes REST API endpoints as Model Context Protocol tools for AI agents. It provides a template for wrapping upstream APIs and includes deployment support for Docker and OpenShift.
      -
    • F
      license
      Not graded
      quality
      D
      maintenance
      A FastMCP-based server that provides tools for API discovery and execution, hierarchical category management, and SQL query execution through the Model Context Protocol.
      -