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mitchallen

random-mcp-server

by mitchallen

random-mcp-server

GitHub tag Docker Hub test bdd

An MCP server that returns random JSON things — people, words, values, coordinates, and an always-empty list. It is a Python / FastMCP adaptation of the Node/Express random-server REST API: each REST route family becomes an MCP tool.

Built with Python, uv, FastMCP, and make.


How the REST API maps to MCP

The REST server seeds a fixed pool of records when it starts and serves them at /v1/<kind>, /v1/<kind>/count, and /v1/<kind>/:id. This server does the same, exposing the pool through a small set of tools parameterized by kind (people, words, values, coords, empty):

REST route

MCP tool

GET /

server_info()

GET /v1/<kind>

list_records(kind, count?)

GET /v1/<kind>/count

count_records(kind)

GET /v1/<kind>/:id

get_record(kind, id) (1-based)

(restart to reseed)

regenerate(seed?)

Records are seeded at start-up, so a given id is stable until you call regenerate. Pass a fixed seed (tool arg or RANDOM_SEED) for reproducible data.

Differences from random-server

  • No x-api-key auth. The REST server's optional API_KEY guard on /v1 routes is not ported — MCP transports handle authentication differently (add FastMCP auth if a networked deployment needs it). The APP_NAME, seeding, and per-kind count behavior are preserved.

  • No Swagger UI. The /api-docs explorer has no MCP equivalent; tool schemas are discoverable through the MCP protocol itself (e.g. make dev).

Example records

// get_record(kind="people", id=1)
{ "type": "people", "prefix": "Mr.", "first": "Augustus", "last": "Gomez",
  "age": 42, "birthday": "7/8/1959", "gender": "male", "zip": "74948-0928",
  "ssnFour": "0791", "phone": "(509) 504-8066", "email": "zeti@tipe.cv" }

// get_record(kind="words", id=1)   -> { "type": "words", "value": "cezuwdi" }
// get_record(kind="values", id=1)  -> { "type": "values", "name": "dafe", "value": -415365907192.2176 }
// get_record(kind="coords", id=1)  -> { "type": "coords", "latitude": 88.43647, "longitude": -93.31203 }

Related MCP server: whoami-mcp

Quick start

Requires uv.

make install     # create .venv and sync deps
make test        # run the test suite
make run         # run the server over stdio

make help lists every target.


Running the server

stdio (default — for MCP clients that launch the server)

uv run random-mcp-server
# or
make run

Streamable HTTP (for networked clients / containers)

make run-http            # PORT defaults to 8000
PORT=9000 make run-http

Inspect the server

make inspect             # print a summary: name, version, tool count
make dev                 # launch the interactive FastMCP Inspector (web UI)

Configuration

All configuration is via environment variables:

Variable

Default

Purpose

APP_NAME

random-mcp-server

Name reported by server_info

RANDOM_COUNT

25

Records generated per kind at start-up

RANDOM_SEED

(random)

Fixed seed for reproducible pools

MCP_TRANSPORT

stdio

stdio, http, or sse

HOST

127.0.0.1

Bind address for http/sse

PORT

8000

Bind port for http/sse


Using with an MCP client — local development (from source)

This section is for developers working from a checkout of this repo. It runs the server straight from your local source via uv, so code changes take effect on the next launch. If you only have the Docker image or a remote deployment, skip to Using a published image or a remote server.

Point a stdio-based client (e.g. Claude Desktop, Claude Code) at the console script. Example claude_desktop_config.json entry using uv:

{
  "mcpServers": {
    "random": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/random-mcp-server", "random-mcp-server"]
    }
  }
}

With Claude Code:

claude mcp add random -- uv run --directory "$PWD" random-mcp-server

Confirm it's connected with claude mcp list (or /mcp inside a session).

Example prompts (Claude Code)

Once the server is added, just ask in plain language — Claude picks the right tool. The tool it invokes is shown in parentheses.

  • "Is the random server up? What version is it?" → (server_info)

  • "Give me 3 random people." → (list_records with kind="people", count=3)

  • "Show me the first random person." → (get_record with kind="people", id=1)

  • "How many random coordinates are available?" → (count_records with kind="coords")

  • "List all the random words." → (list_records with kind="words")

  • "Grab 5 random coordinates and drop them on a map." → (list_records with kind="coords", count=5)

  • "Reseed the random data with seed 42, then show me person 1." → (regenerate with seed=42, then get_record)

  • "Reshuffle all the random records." → (regenerate)

  • "Get random value number 4." → (get_record with kind="values", id=4)

Handy because records are seeded and stable: ask for a person by id, use it to seed a test fixture, and it stays the same until you ask Claude to reseed. Pass a fixed seed (e.g. "reseed with 42") when you need reproducible data.


Using a published image or a remote server

This section is for consumers who are not building from source — you have the published Docker image, or someone has deployed the server for you. No Python, uv, or checkout required. Pick the option that matches how the server reaches you.

Option A — Docker image, client launches it (stdio)

The client starts a fresh container per session and talks to it over stdio. Use -i (keep stdin open) and force the stdio transport, since the image defaults to HTTP:

{
  "mcpServers": {
    "random": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "MCP_TRANSPORT=stdio",
               "ghcr.io/mitchallen/random-mcp-server:latest"]
    }
  }
}

Claude Code equivalent:

claude mcp add random -- docker run -i --rm -e MCP_TRANSPORT=stdio ghcr.io/mitchallen/random-mcp-server:latest

(Swap in mitchallen/random-mcp-server:latest to pull from Docker Hub, or pin a version like :0.1.3.)

Option B — Long-running container over HTTP (local)

Start the container once (it serves HTTP by default), then point an HTTP-capable client at it:

docker run -d --rm -p 8000:8000 --name random-mcp ghcr.io/mitchallen/random-mcp-server:latest

Claude Code (native HTTP transport):

claude mcp add --transport http random http://localhost:8000/mcp/

For clients that only speak stdio, bridge to the HTTP endpoint with mcp-remote:

{
  "mcpServers": {
    "random": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp/"]
    }
  }
}

Option C — Remote deployment (HTTP)

If the server is hosted elsewhere, use its public URL — everything else matches Option B:

claude mcp add --transport http random https://random-mcp.example.com/mcp/
{
  "mcpServers": {
    "random": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://random-mcp.example.com/mcp/"]
    }
  }
}

Notes for remote use:

  • Prefer HTTPS so traffic (and any auth headers) are encrypted in transit.

  • This server ships no authentication (the REST server's x-api-key guard isn't ported). If you expose it beyond localhost, put it behind a reverse proxy, gateway, or network policy that enforces access — or add FastMCP auth.

  • The endpoint path is /mcp/ (note the trailing slash).

The example prompts above work the same once the server is connected by any of these methods.


Verified client setups

The setups below were exercised against the published image (:0.1.3) with a real client — connect, initialize, list tools, and call a tool — not just assumed. Legend: ✅ connected end-to-end · ☑️ server/endpoint proven, that exact client wiring not run here.

Setup

Transport

How it was verified

Status

Docker image, client-launched (Option A)

stdio

Piped an MCP initialize into docker run -i -e MCP_TRANSPORT=stdio …; got a valid response reporting v0.1.3.

Long-running HTTP container (Option B)

HTTP

FastMCP network client against http://localhost:8000/mcp/ (listed all 5 tools, called get_record/count_records).

Long-running HTTP container, Claude Code

HTTP

claude mcp add --transport http …claude mcp list reported ✔ Connected.

Local dev, console script (from source)

stdio

Server proven through the in-memory FastMCP client and the test suite; the uv run stdio launch is the same entry point.

☑️

Remote deployment (Option C)

HTTP

Identical to Option B but with a public URL; the HTTP endpoint is proven, a hosted instance was not stood up.

☑️

stdio-only client via mcp-remote bridge

HTTP (bridged)

Documented from standard mcp-remote usage; not run here.

☑️


Docker

Published multi-platform (linux/amd64, linux/arm64) images are available from two registries:

  • GitHub Container Registry: ghcr.io/mitchallen/random-mcp-server

  • Docker Hub: mitchallen/random-mcp-server

The image runs the server over streamable HTTP by default (MCP_TRANSPORT=http, HOST=0.0.0.0, PORT=8000) so it's reachable on a published port.

Pull the image

docker pull ghcr.io/mitchallen/random-mcp-server:latest
# or from Docker Hub
docker pull mitchallen/random-mcp-server:latest

Both registries also publish version tags (e.g. :0.1.0); prefer a pinned version over :latest for reproducible deployments.

Run the container

docker run --rm -p 8000:8000 --name random-mcp ghcr.io/mitchallen/random-mcp-server:latest

Then connect an HTTP MCP client to http://localhost:8000/mcp/.

Test a published release with make

Convenience targets pull and run the published image in your local Docker environment — handy for smoke-testing a release without a local build:

make docker-test               # up + smoke + down in one shot (exits non-zero on failure)

make docker-up                 # pull + run ghcr.io/mitchallen latest, detached
make docker-smoke              # MCP `initialize` handshake — passes if the server responds
make docker-logs               # follow the container logs
make docker-down               # stop it

make docker-up TAG=0.1.1                         # pin a version
make docker-up REGISTRY=docker.io/mitchallen     # pull from Docker Hub instead
make docker-up HTTP_PORT=9000                    # publish on a different host port

Configure at runtime

Pass any of the configuration variables with -e:

docker run --rm -p 9000:9000 \
  -e PORT=9000 \
  -e APP_NAME=my-random \
  -e RANDOM_COUNT=50 \
  -e RANDOM_SEED=42 \
  ghcr.io/mitchallen/random-mcp-server:latest

To run over stdio inside the container instead (e.g. when another process attaches to it), override the transport:

docker run --rm -i -e MCP_TRANSPORT=stdio ghcr.io/mitchallen/random-mcp-server:latest

Build locally

make docker-build        # docker build -t random-mcp-server .
make docker-run          # serves http on localhost:8000

CI / Publish

Two GitHub Actions workflows live in .github/workflows/:

  • test — runs on every push/PR to main: the unit suite (uv sync --frozen then pytest --ignore=tests/test_bdd.py).

  • bdd — runs on every push/PR to main in its own workflow: the pytest-bdd scenarios (pytest tests/test_bdd.py), so they pass or fail and report (and badge) independently of the unit suite.

  • publish — triggered by pushing a v* tag. Builds a multi-platform (linux/amd64, linux/arm64) image and pushes it to the GitHub Container Registry as ghcr.io/mitchallen/random-mcp-server with both the version and latest tags, then runs make docker-test against the just-published image as a post-publish smoke check (the job fails if the released image doesn't answer an MCP initialize). It uses the built-in GITHUB_TOKEN, so no extra secrets are needed.

  • publish-dockerhub — also triggered by a v* tag. Pushes the same multi-platform image to Docker Hub as mitchallen/random-mcp-server, runs the same make docker-test post-publish smoke check against it, and syncs this README to the Docker Hub repo description. Requires two repository secrets and a pre-created Docker Hub repository (see below).

Docker Hub setup

The publish-dockerhub workflow needs:

  1. A Docker Hub repository named mitchallen/random-mcp-server.

  2. Two repository secrets — set them with the GitHub CLI:

    gh secret set DOCKERHUB_USERNAME --repo mitchallen/random-mcp-server
    gh secret set DOCKERHUB_TOKEN    --repo mitchallen/random-mcp-server   # a Docker Hub access token

Until both secrets exist, the publish-dockerhub job will fail on tag pushes while the GHCR publish job continues to work on its own.

To cut a release, use the release target — it bumps version in pyproject.toml (and uv.lock), commits, tags, and pushes, which triggers both publish workflows:

make release              # patch bump (default)
make release BUMP=minor   # or minor / major

The target refuses to run unless the working tree is clean and you're on main. It's equivalent to bumping the version, then git tag vX.Y.Z && git push origin main vX.Y.Z by hand.


Development

  • Source: src/random_mcp_server/

    • generators.py — deterministic, seedable record builders (RandomFactory)

    • server.py — FastMCP tools + entry point (main)

  • Tests: tests/, run with make test (uv run pytest), driven through an in-memory FastMCP client. Two layers:

    • test_generators.py / test_server.py — plain pytest unit tests.

    • test_bdd.py + tests/features/*.feature — a pytest-bdd layer that mirrors random-server's Cucumber features (Gherkin scenarios for each record kind and the server info check), plus scenarios for get_record (by id, stability, out-of-range), count_records, and regenerate (seed reporting and reproducibility). The /v1/<kind> routes map to the list_records / get_record / count_records tools; auth.feature is not mirrored since the x-api-key guard isn't ported.

  • make build produces a wheel/sdist via uv build.

  • Dependencies: uv.lock is committed and the Docker build installs from it with --frozen. Whenever you change dependencies in pyproject.toml, run make lock (or uv lock) to refresh the lockfile and commit it.

FastMCP integration notes

Two non-obvious adjustments were needed so the FastMCP tooling reports the project correctly:

  • Explicit version. FastMCP(...) is constructed with version=APP_VERSION (read from the installed package metadata). Without it, FastMCP falls back to reporting its own framework version in the MCP initialize handshake and in make inspect / fastmcp inspect — so the server would advertise e.g. 3.4.3 instead of the package's 0.1.2.

  • Absolute import in server.py. The module imports its siblings as from random_mcp_server.generators import ... rather than from .generators. The FastMCP CLI (make inspect, fastmcp list/call) loads server.py by path rather than as part of the installed package, and a relative import fails that way with attempted relative import with no known parent package. The absolute form loads correctly both by path and as a package module.


License

MIT © Mitch Allen

A
license - permissive license
-
quality - not tested
B
maintenance

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