Random Server

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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.

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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?) — opt-in

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.

The regenerate tool is disabled by default — see The regenerate tool is opt-in.

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": "[email protected]" }

// 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 }

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Determinism & idempotency of records

Each server process builds one pool of records per kind when it starts, then serves that pool for the life of the process. What stays stable and what changes follows from that:

• Within a running instance, records are idempotent. For as long as the process is up, get_record(kind, id) returns the same record, and list_records returns the same pool in the same order. Ask for person 1 today and it's identical on the next call — until the pool is rebuilt (below).
• A reboot/restart reshuffles — unless you pin a seed. With no RANDOM_SEED set, the factory picks a random seed at start-up, so restarting the server (or launching a fresh container) yields a different pool. Set RANDOM_SEED to a fixed value and every restart rebuilds the same pool, so records survive reboots.
• A shared instance shows everyone the same records. The pool lives in the process's memory, so all clients connected to the same long-running instance (Option B / C) see the same records — no seed required for them to agree, because they're reading one shared pool. Two separate unseeded instances (or two people each running their own) will not match unless both pin the same RANDOM_SEED.
• Client-launched stdio gets a fresh pool per session. When a client starts the server itself (local dev or Docker/stdio, Option A), each session is its own process with its own start-up seed. Unseeded, those sessions won't agree with each other; set RANDOM_SEED to make them line up.
• regenerate rebuilds the shared pool for everyone on that instance. regenerate(seed=N) is reproducible (same seed → same pool); regenerate() with no argument picks a new random seed. Either way it mutates the shared state, so on a multi-client instance it changes the records every other connected client sees too. Because of that it is disabled by default (see below).
• server_info reports the active seed. Capture that value and feed it back via RANDOM_SEED (at start-up) or regenerate(seed=…) to reproduce a pool you liked later.

Scenario	Same records?
Same instance, repeated calls, no restart	✅ Yes
Same long-running instance, different clients	✅ Yes
After a restart / new container, no RANDOM_SEED	❌ No (reshuffled)
After a restart / new container, fixed RANDOM_SEED	✅ Yes
Two separate unseeded instances	❌ No
Two instances, both pinned to the same RANDOM_SEED	✅ Yes
After regenerate() (no seed)	❌ No (reshuffled)
After regenerate(seed=N) with a seed you used before	✅ Yes

The two regenerate rows assume ALLOW_REGENERATE is set; otherwise the tool is unavailable (opt-in) and only a restart rebuilds the pool.

The regenerate tool is opt-in

Because regenerate reseeds the one shared pool the whole process serves, on a multi-user instance a single caller can reshuffle the records out from under everyone else — no isolation, last-writer-wins. To prevent that, the tool is disabled by default: when ALLOW_REGENERATE is unset the tool is not registered at all, so it doesn't appear in the tool list and can't be called (server_info reports "allow_regenerate": false).

Enable it only where reseeding is safe:

ALLOW_REGENERATE=1 make run        # single-user stdio
docker run --rm -p 8000:8000 -e ALLOW_REGENERATE=1 ghcr.io/mitchallen/random-mcp-server:latest

Guidance by deployment shape:

• Shared long-running instance (Option B / C) — leave it off. Pin RANDOM_SEED if you need a specific reproducible pool, and treat the data as read-only. This is why the Docker image (which defaults to shared HTTP) ships with regenerate off.
• Per-session / single-user (client-launched stdio or local dev) — each client gets its own process, so reseeding only affects that caller. Safe to set ALLOW_REGENERATE=1.

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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.

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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)

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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
ALLOW_REGENERATE	(off)	Expose the regenerate tool (see below)
MCP_TRANSPORT	stdio	stdio, http, or sse
HOST	127.0.0.1	Bind address for http/sse
PORT	8000	Bind port for http/sse

ALLOW_REGENERATE accepts 1/true/yes/on (case-insensitive) to enable.

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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.

The two regenerate prompts only work when the server was started with ALLOW_REGENERATE=1 (the opt-in flag); otherwise the tool isn't exposed and Claude won't have it to call.

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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. The image is published to two registries, so pick one:

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

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

Claude Code equivalent — again, pick a registry:

# GitHub Container Registry (GHCR)
claude mcp add random -- docker run -i --rm -e MCP_TRANSPORT=stdio ghcr.io/mitchallen/random-mcp-server:latest

# Docker Hub
claude mcp add random -- docker run -i --rm -e MCP_TRANSPORT=stdio mitchallen/random-mcp-server:latest

(Pin a version like :0.1.3 in place of :latest for a reproducible setup.)

Scope — local (default) vs user. claude mcp add registers the server in the current project only. Add --scope user (-s user) to register it once for every project on your machine instead:

# GHCR, available across all your projects
claude mcp add --scope user random -- docker run -i --rm -e MCP_TRANSPORT=stdio ghcr.io/mitchallen/random-mcp-server:latest

# Docker Hub, available across all your projects
claude mcp add --scope user random -- docker run -i --rm -e MCP_TRANSPORT=stdio mitchallen/random-mcp-server:latest

(Scopes are local — this project, the default; project — shared via a checked-in .mcp.json; and user — all your projects.)

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

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

# GitHub Container Registry (GHCR)
docker run -d --rm -p 8000:8000 --name random-mcp ghcr.io/mitchallen/random-mcp-server:latest

# Docker Hub
docker run -d --rm -p 8000:8000 --name random-mcp mitchallen/random-mcp-server:latest

Claude Code (native HTTP transport) — the client connects over HTTP, so the command is the same regardless of which registry you pulled from:

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

Add --scope user (-s user) to register it for every project on your machine instead of just the current one:

claude mcp add --scope user --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. There's no image to pull here (the host already runs it, from whichever registry they chose), so registry choice doesn't apply on your side:

claude mcp add --transport http random https://random-mcp.example.com/mcp

Add --scope user (-s user) to register it across all your projects:

claude mcp add --scope user --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.
• No built-in rate limiting — by design (see below).
• The endpoint path is /mcp (no trailing slash). Requesting /mcp/ works too but returns a 307 redirect to /mcp, so point clients at /mcp to skip the extra round-trip.

Why there's no built-in rate limiting

FastMCP ships rate-limiting middleware and it would be a few lines to wire in, but this server deliberately doesn't:

• Nothing expensive to protect. Every tool is an in-memory lookup against a pool built once at start-up — no database, external API, or real compute cost. Rate limiting shields scarce resources; this workload has none.
• It only makes sense on the shared HTTP transport. Over stdio each client launches its own process, so throttling your own single-user server is moot.
• Without auth there's no per-client identity to key on. In-memory limiting would fall back to a global limit, which recreates the shared-instance fairness problem (one noisy client starves everyone) rather than solving it. It also wouldn't coordinate across replicas behind a load balancer.
• The edge is the right layer. For a public deployment, enforce rate limits at the same reverse proxy / gateway that provides auth and TLS (nginx, Cloudflare, an API gateway) — it coordinates across replicas and can key on authenticated identity. App-level limiting becomes worthwhile mainly once you add auth (so get_client_id is meaningful).

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

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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.	☑️

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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.

It's built on a distroless Chainguard/Wolfi Python base — no shell or package manager, runs as a non-root user, and scans 0 known vulnerabilities. Every build is gated by a Trivy scan (fails on fixable CRITICAL/HIGH) and the published :latest is re-scanned daily; see CI / Publish.

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. :0.2.4); 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

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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.

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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.

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License

MIT © Mitch Allen