transformatron

Build Maltego transforms with a coding agent, against a real server.

Maltego transforms are small functions that take one entity (an IP address, a domain, a person) and return related entities, building up a graph. This project gives an agent the two halves it needs to build them: a local transform server it can drive — start, reload after an edit, run a transform and read back what it returned — and the authoring guidance and scaffolding to write the transform in the first place.

That combination is the point. An agent that can only write code guesses at what the API returns; an agent that can only run a server has nothing to run. Together they close the loop: scaffold from a spec, restart, run it, see the entities, fix what the spec got wrong.

Everything works two ways: an MCP server for agents that speak it, and a CLI for humans and any agent that can run commands. Both call the same code, so they behave identically.

Status: early. Built and verified against a live server on macOS, but not yet exercised on Linux or Windows, and not published to PyPI. Expect rough edges.

Requirements

• Python 3.13+

• uv for dependency management

• A Maltego client — Maltego Desktop to actually use the transforms. Not needed to run the tests.

• OpenSSL on PATH (ships with macOS and most Linux distributions) — only for HTTPS certs.

Related MCP server: mcdev-mcp

Quick start

git clone <your-fork-url> claude-transformatron-9000
cd claude-transformatron-9000
uv sync
uv run pytest -q

That verifies the control plane. Then go to Build your first transform — with an agent or by hand — and, if you have API keys to test against, Credentials.

Connecting to the Maltego desktop client

The desktop client refuses plain-HTTP transform servers, so HTTPS is not optional:

Only HTTPS (SSL/TLS) Transform Servers are allowed, but found: http://127.0.0.1:3000

Note that this failure happens inside the client — the request never reaches the server, so nothing appears in the server log. An empty log alongside a client-side error is the signature of this problem, not a sign the server is broken.

1. Generate a self-signed certificate (generate_certs).

2. Trust it. This changes system trust, so the tool prints the command rather than running it:

   sudo security add-trusted-cert -d -r trustRoot \
     -k /Library/Keychains/System.keychain .transformatron/certs/cert.pem

   That command is macOS-specific; on Linux, install the cert into your distribution's CA store.

3. Start the server with SSL (server_restart(ssl=True)).

4. In Maltego: Transforms → Transform Hub → add a local hub item, and paste the seed URL https://127.0.0.1:3000/seed.

5. Install the hub item, then right-click a matching entity to run a transform.

The server binds to 127.0.0.1 only. That is fine for a client on the same machine, but a client on another host will not reach it.

Build your first transform

Assumes uv sync has run.

With an agent

Open a coding agent in the clone and start the server:

uv run python scripts/transformatron_cli.py start

Claude Code picks up .claude/skills/maltego-transform-author/ from the clone; other agents read AGENTS.md. Nothing to invoke — the guidance is already loaded. Then describe what you want:

Build a transform for urlscan.io's search endpoint, then run it and show me the entities: curl "https://urlscan.io/api/v1/search/?q=domain:example.com"

Here is the whole session, and what the agent is doing at each step.

1. It reads docs/transform-authoring.md first. Two SDK behaviours fail silently — see Writing transforms — and the skill treats reading those corrections as a precondition for writing code.

2. It scaffolds:

Scaffolded 'Urlscan' (urlscan):

Created files:
  - server/transforms/urlscan/__init__.py
  - server/transforms/urlscan/api.py
  - server/transforms/urlscan/lookup.py

Transforms:
  - Urlscan: Lookup (IPv6Address -> Phrase)

Import added to server/project.py.

Note IPv6Address -> Phrase, which is wrong on both sides: the endpoint takes a search query and returns a list of scans. The scaffolder had a URL and nothing else, so it guessed from the parameter name and guessed badly. Correcting that is the next three steps, and it is the normal case rather than a mishap — pass --sample-response with real JSON and the output side improves, but only a live run settles it.

3. It restarts and confirms registration — restart, then list. The server only loads code at startup, so an edit without a restart changes nothing. A transform missing from list almost always means a missing import in project.py, which scaffold writes for you.

4. It runs the transform and reads the output. The generated code maps one Phrase out of the response, so the first run reports something like:

State: COMPLETED (success)
Entities (1):
  {"type": "maltego.Phrase", ...}

This is the step that makes the difference. COMPLETED (success) is not the answer — the entity count is. One Phrase holding a blob of text is not a useful transform: urlscan returns a list of scans, each with a page URL, an IP and an ASN. The agent now knows the real response shape, which the cURL command never told it.

5. It corrects the mapping and goes round again. Take the input as a search Phrase, walk results[], map page.url to a URL, page.ip to an IPv4Address, page.domain to a Domain and page.asn to an AS. Restart, run, read the entities again — the finished transform returns 25 for this query rather than 1. Two or three passes is normal.

6. It gates the result:

uv run python scripts/smoke_test_transforms.py

Runs every registered transform and fails on zero entities or an output type of NONE.

What you decide

The agent handles the mechanics. The judgement calls are yours:

• Which API, and which endpoints deserve transforms.

• How responses map to entities. Is page.asn an AS entity or a Phrase? That choice decides whether the graph can pivot on it.

• When it is actually done. The agent may accept one entity; you know the query should have returned twenty.

One habit is worth more than the rest: when an agent says a transform works, ask what the entity count was. That single question catches the failure mode this project exists to prevent.

If the MCP tools are missing

A fresh clone prompts once for approval of the transformatron MCP server and needs a session restart to load it. Until then the agent falls back to the CLI, which does exactly the same things. Seeing it shell out to transformatron_cli.py instead of calling run_transform is expected, not a fault.

By hand

The same loop, driven yourself. Scaffold from a spec:

uv run python scripts/transformatron_cli.py scaffold --service ipinfo \
  --curl 'curl -H "Authorization: Bearer TOKEN" https://ipinfo.io/8.8.8.8/json'

Or write a module under server/transforms/ directly — this is the minimal shape:

# server/transforms/hello.py
from maltego.entities import Domain, IPv4Address
from maltego.model.context import MaltegoContext
from maltego.server import register_transform


@register_transform(display_name="Hello: IP to Domain", transform_set="hello")
async def ip_to_domain(input_entity: IPv4Address, context: MaltegoContext) -> list[Domain]:
    """Return a fixed domain, to prove the loop works."""
    return [Domain(value="example.com")]

Both annotations matter: IPv4Address declares the input type, list[Domain] the output. A bare -> list registers the transform with output NONE and the client cannot route it.

A hand-written module needs its import in server/project.py, next to the existing ones (scaffold does this for you):

from transforms.hello import *  # noqa: F401,F403

The server only loads what project.py imports, and the import must sit with the others at the top — appending it to the end of the file puts it after the if __name__ == "__main__" block, where it still runs but registers nothing you can see. A transform that never appears in list is almost always this.

# Reload and confirm.
uv run python scripts/transformatron_cli.py restart
uv run python scripts/transformatron_cli.py list

# Run it, and check the entity count — not just the success state.
uv run python scripts/transformatron_cli.py run \
  acme.new_maltego_integration.ip_to_domain maltego.IPv4Address 8.8.8.8

# Gate the whole set.
uv run python scripts/smoke_test_transforms.py

Then connect the desktop client and run it on a real graph.

Scaffolding from a spec

scaffold turns a cURL command or an OpenAPI document into a working module package: an api.py client with the auth and validation wired up, one module per transform, and the import added to server/project.py.

# From a cURL command, with a sample response to infer output entities from.
uv run python scripts/transformatron_cli.py scaffold --service demo \
  --curl 'curl -H "X-API-KEY: k" https://api.demo.com/v1/ip/8.8.8.8'

# From an OpenAPI spec — a path or the document itself.
uv run python scripts/transformatron_cli.py scaffold --service demo --openapi ./demo-openapi.json

It infers input and output entity types from parameter names and the sample response, picks the right validator for the input type, and handles keys sent as a header, a bearer token, or a query parameter.

Treat the result as a first draft. The generator works from the spec, and specs routinely disagree with the live API about which fields are present, what a 404 means, and how errors are shaped. Run the transform, read the entities, and correct the mapping — the loop above exists for exactly this. An existing service is never overwritten: scaffolding onto one raises rather than replacing hand-written code that has already absorbed those corrections.

Writing transforms

Start with docs/transform-authoring.md.

The SDK ships its own authoring guidance in server/.agents/skills/, versioned with the package, and this project does not restate it — a copy would go stale. But two of its examples are wrong in ways that fail silently: the code looks right, the run reports success, and no entities come back. docs/transform-authoring.md corrects those, then routes to the SDK guidance for everything else.

The loop

1. Add or edit a module under server/transforms/.

2. Import it in server/project.py (from transforms.my_module import *). The server only discovers what project.py imports — this is the most common reason a new transform never shows up.

3. server_restart — this is the reload path. It keeps the scheme the server is already running under, so an HTTPS server stays on HTTPS.

4. list_transforms to confirm it registered, then run_transform to exercise it.

Commands

Every operation is available as a CLI command and as an MCP tool. Both call the same transformatron.operations module, so output is identical.

CLI commands are prefixed uv run python scripts/transformatron_cli.py:

uv run python scripts/transformatron_cli.py restart
uv run python scripts/transformatron_cli.py list
uv run python scripts/transformatron_cli.py run <id> maltego.IPv4Address 8.8.8.8

Pass settings with repeated --setting KEY=VALUE. --help works on any subcommand.

Adding an operation? Put it in src/transformatron/operations.py and both front ends get it.

Credentials

In real use, an API key belongs in the Maltego client's transform settings — declared with TransformSetting(auth=True, is_global=True), entered once, reused by every transform in the set. Nothing needs configuring on the server for that path to work.

For headless runs there is no client to enter it into, so transforms fall back to the process environment. Copy the template and fill in what you have:

cp .env.example .env

.env is read when the server starts and merged into its environment, so a key written once survives restarts. The smoke test reads it too, which is the difference between a credential-gated transform being exercised and being reported SKIP. An exported shell variable beats the file, and an explicit --setting beats both.

.env is gitignored. .env.example is the committed template and holds no values. This is a development convenience: it puts keys in the server process's environment, visible to anyone who can read ps. Restart after editing it — the environment is read once, at start.

Transforms you do not want to publish

Anything under server/transforms/local/ is gitignored and discovered automatically when the server starts. Use it for integrations that should not be committed — an internal API, a client-specific lookup, work in progress.

It is discovered rather than imported by name, because a fresh clone does not have the directory and a static import of a missing module stops the server booting. The committed examples alongside it keep their explicit imports in project.py.

Using it with a coding agent

An agent working in this repository can scaffold a transform from an API spec, restart the server to load it, run it against a real input, read back the entities it produced, and gate the whole set with the smoke test. That is the loop — and because every step reports what actually happened, the agent can tell a working transform from one that reports success and returns nothing.

What it reads:

CLAUDE.md points at AGENTS.md so the two cannot drift. Both route to docs/transform-authoring.md for the SDK corrections.

Any agent that can run shell commands can drive the server through the CLI — no MCP required.

For Claude Code, the MCP server is registered at project scope in .mcp.json, so a fresh clone picks it up automatically. It needs a session restart to load and prompts once for approval. That approval is recorded in .claude/settings.local.json, which is per-machine and gitignored, so a fresh clone prompts again — expected, not a bug. If the tools are unavailable, the CLI does everything they do.

If you are modifying this project's own code under src/transformatron/, note that the MCP tools run the version loaded when the session started, so your edits will not show up there until you relaunch. The CLI always runs current code. AGENTS.md has the details — this does not affect editing transforms under server/transforms/.

Using it from Python

The same operations work directly:

import asyncio
from transformatron import lifecycle
from transformatron.client import TransformClient
from transformatron.config import load_config

config = load_config()
lifecycle.start(config, ssl=True)


async def main() -> None:
    client = TransformClient(lifecycle.resolve_config(config))
    for transform in await client.list_transforms():
        print(transform["name"])


asyncio.run(main())

Run it with uv run python your_script.py.

The sample transforms

Several worked examples ship with the project. All are illustrative samples, not maintained integrations — delete whichever you do not need, along with its import in server/project.py.

Start from ffraud.py if you are learning the shape. Start from ransomwarelive/ if your API needs a key — it is documented in docs/ransomware-live.md and shows the parts the simple example cannot: declaring one credential across a whole transform set, keeping validation and error handling in a shared api.py, capping result sizes so a large upstream response does not flood the graph, and normalising a schema whose field names differ between endpoints. It needs a ransomware.live API key to run.

greynoise/ is worth reading for what a spec cannot tell you: an unrecognised key is accepted silently, so a successful lookup is not evidence the key is valid; HTTP 404 is a real verdict ("never observed scanning") rather than a failure; and the free tier allows roughly 25 lookups a week, which the smoke test can spend in one pass. Every one of those was found by running it, not by reading the documentation.

ffraud

Against ffraud.com, a third-party IP reputation API this project has no affiliation with. It needs no API key, so it runs on a fresh clone.

Three transforms, all taking maltego.IPv4Address:

Worth reading for the patterns it demonstrates: validating input_entity.value before interpolating it into a URL, catching MaltegoException so an upstream outage degrades to an empty result instead of a crash, and using .get() throughout because the API omits fields rather than nulling them.

Caveat on the data: upstream coverage is uneven. A known Tor exit node returned fraud_score: 0, risk: none with tor: false during testing, and sample responses carried data_completeness: 0.5. Treat the scores as illustrative. To delete the sample, remove the directory and its import in server/project.py.

Layout

src/transformatron/
  operations.py  server operations shared by both front ends — add new ones here
  config.py      TransformatronConfig — host, port, scheme, derived URLs and state paths
  client.py      async v3 protocol client; the run→poll→flatten state machine
  lifecycle.py   start/stop/restart/status/logs over a detached subprocess
  certs.py       self-signed certificate generation
  envfile.py     reads .env so headless runs can reach credentials
  scaffold/      spec → module package: parser.py, generator.py, schema.py
  mcp.py         MCP front end
scripts/
  transformatron_cli.py     CLI front end
  smoke_test_transforms.py  runs every transform, fails on zero entities
server/          SDK-generated (`maltego-transforms start server --with-skills`).
                 Upstream-owned except transforms/: .agents/ and project.py are
                 excluded from ruff so regeneration does not churn.
  project.py     entrypoint; imports decide what gets registered
  transforms/    your transform modules go here — linted like the rest of the project
    local/       gitignored; discovered at startup, for integrations you do not publish
.claude/skills/
  maltego-transform-author/  authoring checklist; points at docs/, not a second copy
docs/
  transform-authoring.md   read before writing a transform
  ransomware-live.md       the authenticated worked example
  ipinfo.md                bearer auth, one response to several entities
  upstream-sdk-issue.md    draft bug report, not yet filed
tests/           unit tests for the control plane and the scaffolder
.env             API keys for headless runs. Gitignored; .env.example is the template.
.transformatron/ runtime state — PID, log, certs, recorded scheme. Gitignored.

Gotchas

Things that cost real debugging time, recorded so they cost you less:

• A transform missing from list_transforms, or showing output type NONE, almost always means a missing or untyped annotation. Input type comes from the parameter annotation, output from the return annotation. A bare -> list advertises no output type and breaks client routing.

• Returning a MaltegoGraph from an async transform silently yields zero entities. The run still reports success. In the SDK's __handle_async_result, a returned graph fails both isinstance branches and is dropped; the generator branch skips graphs too, so this affects every async path. Return a list — -> list[AS | ISP] still publishes every output type to discovery. This contradicts the SDK's own shipped guidance, which teaches the broken pattern; see docs/transform-authoring.md and the draft report in docs/upstream-sdk-issue.md.

• A success state is not proof a transform works. Check the entity count, or run scripts/smoke_test_transforms.py.

• An API key entered in the client can stop reaching the transform after a seed re-import. The symptom is a transform reporting the key as missing while the client's settings field still looks populated. Re-importing after the seed URL changes — switching the server between HTTP and HTTPS does this — rewrites the transform definitions and orphans the stored global value. Clear the field, apply, then re-enter the key. See docs/transform-authoring.md for the environment-variable fallback that avoids this during development.

• A successful lookup is not proof the API key is valid. Some upstreams accept an unrecognised key and answer normally — GreyNoise does — so there is no auth-failure path to catch and no signal that the key is wrong until a quota or a permission boundary exposes it. Verify a key against something that requires it, not against a call that happens to succeed.

• The desktop client requires HTTPS (see above).

• The server runs on port 3000. The SDK's own skill scripts default to 8080 — pass --port 3000 if you invoke them directly.

• MALTEGO_SERVER_* environment variables take precedence over the values hardcoded in project.py. That is how the lifecycle tools set host, port, and scheme without editing the generated file.

• The scheme is runtime state, recorded in .transformatron/server.scheme. project.py defaults to https, but the lifecycle tools override the scheme per start, so that file is the only way the control plane knows which scheme to address a running server over.

Development

uv run pytest -q
uv run ruff check . && uv run ruff format --check .
uv run ty check src tests scripts

server/.agents/ and server/project.py are excluded from linting: the SDK owns them, and regenerating would churn against upstream. server/transforms/ is your code and is linted like the rest of the project — including anything scaffold generates.

Smoke-testing transforms

uv run python scripts/smoke_test_transforms.py
uv run python scripts/smoke_test_transforms.py --setting API_KEY=xxx

Runs every registered transform against a sample input and fails on zero entities or an output type of NONE — the failure modes that otherwise report success. Exits non-zero, so it works as a gate. Run it after any change under server/transforms/.

Transforms needing credentials take them through repeated --setting KEY=VALUE, or from .env (see Credentials). Four outcomes report SKIP rather than FAIL — a missing credential, an upstream rate limit, a sample input the transform's own validation rejects, and an input the upstream has no match for — so neither an unconfigured key nor a spent quota is mistaken for broken code. A SKIP is not evidence a transform works; it means the gate could not judge it. Add a TRANSFORM_SAMPLES entry when the per-entity-type sample does not suit a transform.

Transforms calling third-party APIs make live network requests, so a failure can mean an upstream outage rather than broken code; check the reported message. Override the input with --value, or check one transform with --transform <id>.

Naming

The generated server still identifies itself with the SDK's placeholders — server_name="New Maltego Integration", ns="acme.new_maltego_integration", author="Acme Corp" in server/project.py. Change these before publishing anything; the namespace becomes part of every transform's fully qualified ID.

License

MIT — see LICENSE.

Maltego is a trademark of Maltego Technologies GmbH. This is an independent project, not affiliated with or endorsed by Maltego.