Skip to main content
Glama
hexacron

transformatron

by hexacron
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

transformatron

CI

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. Verified on macOS and Linux: CI runs the full suite on Ubuntu and macOS with Python 3.13 and 3.14, and a daily job starts a live server on Ubuntu and runs the keyless transforms against their real APIs. The Maltego desktop client has been exercised on macOS only. Windows is not supported — the lifecycle relies on POSIX signals, and its os.kill(pid, 0) liveness probe would terminate the server there. 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 https://github.com/hexacron/transformatron.git
cd transformatron
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 by default, which reaches a client on the same machine and nothing else. host and port are set in transformatron.toml (see Naming). Binding a non-loopback address such as 0.0.0.0 makes the server reachable from other machines, but two things do not follow it:

  • The certificate from generate_certs covers only localhost and 127.0.0.1, so a client dialling any other address rejects it. A remote client needs a certificate for the address it dials, placed at .transformatron/certs/cert.pem and key.pem.

  • The seed URL is built from the bind address. With 0.0.0.0 it reads https://0.0.0.0:3000/seed, which another machine cannot dial — substitute the machine's reachable address when registering the hub item.

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, run against the live API on 2026-10-11, and what the agent is doing at each step. urlscan's results change by the minute, so yours will differ.

1. It reads docs/transform-authoring.md first. The SDK's own guidance is wrong in two places, one of which fails silently — see Writing transforms — and the skill treats reading those corrections as a precondition for writing code.

2. It fetches a real response and dry-runs the scaffold:

uv run python scripts/transformatron_cli.py scaffold \
  --curl 'curl "https://urlscan.io/api/v1/search/?q=domain:example.com"' \
  --sample-response "$(curl -s 'https://urlscan.io/api/v1/search/?q=domain:example.com')" \
  --dry-run
Transforms:
  - acme.new_maltego_integration.urlscan_lookup: Urlscan: Lookup
      Phrase -> IPv4Address, URL, Domain, AS, Image (GET /api/v1/search/)
      sample input: domain:example.com

Field inventory, acme.new_maltego_integration.urlscan_lookup (path, sample, outcome):
  results[].submitter                {}                                dropped: empty in the sample
  results[].canonical.task.url       secccuurrrreeee.ink               entity URL
  results[].canonical.page.url       example.com                       entity URL
  ...
  results[].page.ip                  172.66.147.243                    entity IPv4Address (primary)
  ...
  results[].page.asn                 AS13335                           entity AS
  ...
  results[].result                   https://urlscan.io/api/v1/res...  entity URL
  results[].screenshot               https://urlscan.io/screenshot...  entity Image
  total                              5605                              dropped: envelope
  took                               2009                              dropped: envelope
  has_more                           False                             dropped: envelope
  search_date_limit_days             30                                dropped: no primary entity (list fields and input echoes are never primary); --map a single field to an entity type, or pick one with --primary
...
Next steps:
...
3. Run the same command without --dry-run to write it, restart the server, and run each transform against its sample input:
     uv run python scripts/transformatron_cli.py run acme.new_maltego_integration.urlscan_lookup maltego.Phrase domain:example.com

Nothing is written yet. The input side is right: q carries a search query, so the transform takes a Phrase. Each scan becomes one IPv4Address carrying the scan's plain fields as properties, with its domains, URLs and AS beside it. The inventory shows what to fix: canonical.*.url holds bare hostnames typed as URLs, result and screenshot point at urlscan itself rather than the scanned site, and search_date_limit_days sitting beside results made the whole response one record.

3. It fixes the mapping and writes it — --records says where the records are, --map drops the noise:

uv run python scripts/transformatron_cli.py scaffold \
  --curl 'curl "https://urlscan.io/api/v1/search/?q=domain:example.com"' \
  --sample-response "$(curl -s 'https://urlscan.io/api/v1/search/?q=domain:example.com')" \
  --records results --map canonical.page.url=Drop --map canonical.task.url=Drop \
  --map result=Drop --map screenshot=Drop
Transforms:
  - acme.new_maltego_integration.urlscan_lookup: Urlscan: Lookup
      Phrase -> IPv4Address, Domain, URL, AS (GET /api/v1/search/)
      sample input: domain:example.com
...
Notes:
  ...
  - The request looks paginated (response field(s) total, has_more): the transform reads the first page only, and at most MAX_ITEMS records of it (api.py). Add paging in the module if one page is not enough.

Imports added to server/project.py.

Next steps:
1. Run 'transformatron_cli.py restart' to load the new transforms.
2. Run each against its sample input and compare the entities with the field inventory above: the entity count, the types, and the values.
     uv run python scripts/transformatron_cli.py run acme.new_maltego_integration.urlscan_lookup maltego.Phrase domain:example.com
...

The id the server registers carries the namespace, and the printed run line is ready to paste.

4. It restarts and confirms registration — restart, then list:

acme.new_maltego_integration.urlscan_lookup
    Urlscan: Lookup
    maltego.Phrase -> maltego.AS, maltego.URL, maltego.Domain, maltego.IPv4Address

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.

5. It runs the transform on the sample input and reads the output:

State: COMPLETED (success)
Entities (41, 41 distinct):
  maltego.IPv4Address  172.66.147.243  {task.visibility=public, task.method=api, task.time=2026-10-11T17:38:19.025Z, 2026-10-11T..., task.uuid=01a12c0b-2dc1-722d-b687-4f459ed2d372,..., task.tags=@ecarlesi, possiblethreat, phishing, ..., stats.uniqIPs=2, stats.uniqCountries=2, stats.dataLength=2956, +24 more}  [page.ip]
  maltego.Domain  secccuurrrreeee.ink  [task.domain]
  maltego.URL  https://secccuurrrreeee.ink/  [task.url]
  maltego.URL  https://example.com/  [page.url]
  maltego.Domain  example.com  [page.domain]
  maltego.AS  13335  [page.asn]
  maltego.IPv4Address  2606:4700:3031::ac43:a78e  {task.visibility=public, task.method=api, task.time=2026-10-11T17:32:10.143Z, task.uuid=01a12c05-89d6-7569-b742-80203e916bbb, stats.uniqIPs=6, stats.uniqCountries=4, stats.dataLength=334590, stats.encodedDataLength=212602, +14 more}  [page.ip]
  ...
Links: 41

This is the step that makes the difference. COMPLETED (success) is not the answer — the entities are. Read the count against the distinct count, the message levels (none here), the properties and the types. Three things stand out: the properties carry noise nobody pivots on (ids, byte counts); page.ip holds IPv6 addresses as well, typed IPv4Address — the scaffolder gives a field one type; and 172.66.147.243 served 12 of the first 20 scans in the sample, so it is one entity whose properties hold every scan's values, joined with , .

6. It edits the mapping and goes round again. The module's RECORDS table is the mapping: deleting the Prop lines for _id, _score, sort, task.uuid and the two byte counts drops those properties. Restart, run, read again:

  maltego.IPv4Address  2606:4700:3031::ac43:a78e  {task.visibility=public, task.method=api, task.time=2026-10-11T17:32:10.143Z, stats.uniqIPs=6, stats.uniqCountries=4, stats.requests=19, page.server=cloudflare, page.apexDomainAgeDays=61, +9 more}  [page.ip]

Two or three passes is normal.

7. It gates the result:

uv run python scripts/smoke_test_transforms.py --transform acme.new_maltego_integration.urlscan_lookup
  PASS acme.new_maltego_integration.urlscan_lookup
       41 entities from domain:example.com

1 passed, 0 failed, 0 skipped

The input came from the samples.json the scaffold wrote. Without --transform it 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? Should an IPv6 address in page.ip stay an IPv4Address, become a property, or get code that types it by version? These choices decide 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. Dry-run a scaffold, read the field inventory, then write it:

uv run python scripts/transformatron_cli.py scaffold \
  --curl 'curl https://internetdb.shodan.io/8.8.8.8' \
  --sample-response "$(curl -s https://internetdb.shodan.io/8.8.8.8)" \
  --dry-run

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. With no return annotation the transform registers with output NONE and the client cannot route it; a bare -> list stops the server starting, with an error that blames input_entity.

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 read the entity count and message levels — 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 module package under server/transforms/<service>/: an api.py holding the base URL, auth, headers, input validators, fetch and the mapping helpers; one module per transform holding its RECORDS table; a samples.json; and the import added to server/project.py.

# From a cURL command, with the response from running it. --dry-run writes nothing.
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' \
  --sample-response "$(curl -s -H 'X-API-KEY: k' https://api.demo.com/v1/ip/8.8.8.8)" \
  --dry-run

# From an OpenAPI spec — a URL, a path, or the document itself, in JSON or YAML.
# List the operations first, then scaffold the ones you want. --sample-input names a value
# the API answers for, when the spec gives the input parameter no example.
uv run python scripts/transformatron_cli.py scaffold --service demo \
  --openapi https://api.demo.com/openapi.yaml --list-operations
uv run python scripts/transformatron_cli.py scaffold --service demo \
  --openapi https://api.demo.com/openapi.yaml --operation searchHosts --sample-input 8.8.8.8

What it works out:

  • The input. Whichever part of the request — a path segment, a query parameter, or a JSON or form body field — holds something that looks like an IP, domain, URL, email, hash or CVE, by name or by value. Failing that, a Phrase: a lone query parameter, or the path segment that looks most like a value (0000-0002-1825-0097, not person). A paging parameter, or an optional one in a spec, is never the input. Other query parameters and body fields are kept and sent with every request.

  • The method and body. A cURL command with -d, --json or -X POST scaffolds a POST that sends the body with the input substituted in.

  • The request, as given. Every cURL header is sent, plus -A, -e and -b — except the auth header (it becomes the setting) and connection headers (Host, Content-Length, Accept-Encoding, …). A cookie is copied verbatim, with a note that it may expire. Repeated query parameters stay repeated. What it cannot reproduce — -u, a proxy, -F, client certificates, -k, a cookie-jar file — is listed under Notes.

  • Authentication. A key-like header, a bearer token, or a key-like query parameter (apikey, token, …) becomes a TransformSetting. A request with none of these scaffolds a client that needs no key. OpenAPI schemes it cannot express (OAuth2, basic) are reported, not guessed. Where a spec allows anonymous access — {} among an operation's security requirements, security: [], or no requirement — the key is sent when set and not required.

  • The outputs, from the sample response (or the OpenAPI response schema or example, with $refs resolved). See The field inventory. JSON only: a CSV or plain-text sample is refused. For such an API, scaffold without --sample-response for the request code, then replace RECORDS and the map_records() call with your own parsing, fetched through api.client directly, since fetch() returns decoded JSON only.

  • The sample input — the value the sample response answers. For cURL it is the value in the request; for OpenAPI, the input parameter's default, first enum value, example, x-example or examples. --sample-input VALUE sets it (one operation at a time). It goes into samples.json and the printed run command, and lets the transform recognise the input echoed back in the response. An OpenAPI scaffold with none gets a Note, no samples.json entry, and VALUE in its run command; pass --sample-input and scaffold again.

Anything it skipped or could not express — an operation with no usable input, a path parameter with no default, an unsupported auth scheme, pagination, request details it dropped — is listed under Notes in its output. Each transform is printed under the id the server registers (<namespace>.<service>_<name>), with a run command to paste.

The field inventory

scaffold prints every leaf of the sample with what happened to it — entity T (primary), entity T, property NAME, or dropped: <reason>, marked (--map) where an override decided it — and --dry-run prints the same without writing. Read it before writing anything.

  • Records. A top-level list is the records; so is a list of objects that is the only substantive key of the response (data, results), also behind up to two wrapper objects ({"data": {"items": [...]}}, Elasticsearch's hits.hits). Envelope keys, summary keys (_shards, aggregations, aggs, facets) and, inside a wrapper, a float, boolean or null beside the list (hits.max_score) do not count. Anything else is one record, and lists of objects inside it become nested records. --records PATH overrides this.

  • One primary entity per record — its strongest single-valued typed field (CVE before IP before domain …). A list field is never primary. A field echoing the input is primary only when the record is about the input (the whole response, or a top-level record whose one typed field it is, as in EPSS); the record's properties then enrich the input entity. The record's plain values become properties on the primary, and its other typed values become entities beside it, linked with a label made from the field's path. A top-level record with no single-valued typed field becomes a Phrase titled by name, title, label, displayname, fullname, summary, id or identifier (with nothing typed at all, failing those, another readable text field). A title must read as one: 4 characters or more, not an ENUM_CASE value or true/false/null/none/n/a, and a number only under id or identifier. With no title there is no primary, and the plain fields are dropped.

  • Types from names. A key such as name, owner, contact, state, region or province types its field only from a real value, so a schema-built sample, which has none, types none of them (no Person from a pet's name).

  • Depth. Typed values are followed 4 levels into a record and properties 3. Deeper fields are dropped, and the inventory says so.

  • Dropped: metadata subtrees (links, meta, …), fields outside the records, empty values, and more than 30 properties. Envelope keys (status, version, total, limit, …) are dropped beside the records; when the whole response is one record, a top-level one is dropped only when another sits beside it — a lone status is the record's own field.

Change it with overrides, and repeat with --dry-run until it maps what an investigator needs:

  • --records PATH — the list of records, as a dotted path: data, containers.cna.references.

  • --map PATH=TYPE (repeatable) — one field. TYPE is an entity class (Company, URL, …), Property to attach it to the record's entity, or Drop. PATH is dotted, from the response root or from the records, with optional []: containers.cna.affected[].vendor=Company. Depth limits and the property cap do not apply, and it works on a field empty in the sample or inside a metadata or envelope subtree. Property on a field in a nested list with no entity of its own puts it on the enclosing record's entity, every value joined with , (containers.cna.descriptions[].value=Property puts the description on the CVE).

  • --primary PATH — the field that is each record's primary entity, written like a --map path. It must be a single-valued entity field, inferred or made one by --map in the same command; the error otherwise lists the fields that can be. Naming an echo of the input enriches the input entity with the record's properties.

A path that matches nothing is an error that points back at --dry-run, and so is a --map that cannot be honoured (a Property with no entity to attach to).

After writing: RECORDS

Each module declares its mapping as a RECORDS = Records(...) table — Entity, Prop and map_records live in the service's api.py. Edit that table by hand to correct the mapping after a live run: delete a line to stop mapping a field, change a type, move a field between props and entities. docs/transform-authoring.md describes it. map_records returns each (type, value) once: met again in another record, the entity gains that record's property values, distinct values joined with , . It splits multi-line values, skips the input's own value, and reads at most MAX_ITEMS (20) records from each list.

Services, names and ids

  • cURL transforms get the id <service>_<name> and the module <name>.py; --name defaults to lookup. To grow a service endpoint by endpoint, scaffold again with the same --service and a new --name, no --force. That is refused unless the regenerated api.py would be byte-identical to the one on disk — same base URL, auth and headers, and not edited by hand — because the existing transforms share it.

  • OpenAPI transforms get <service>_<operation> (getIp → demo_get_ip) and the module <operation>.py. GET operations are scaffolded by default; POST only when named with --operation, as is a GET whose first path-segment word, or first operationId word after a leading get/post/api/do, is a state-changing verb (create, delete, remove, update, edit, login, logout, register, reset, set, add, insert, upload, import, purge, flush, revoke, subscribe, unsubscribe, start, stop). --list-operations shows each operation's method, path, transform id, input, whether it has a response schema, and why it would be skipped. Over 20 operations with no --operation gets a note. A spec with no response schema gives a placeholder output: pass --sample-response with exactly one --operation.

  • An operationId the spec reuses gets a distinct transform id and display name per operation, from the path words or parameters the others lack (get_last_by_number, (by number)), numbered as a last resort. --list-operations marks it with *; --operation with it scaffolds all of them, and its TRANSFORM value picks one.

  • The base URL is the spec's server; a Swagger 2 spec with basePath and no host resolves it against the spec's URL.

  • Ids are global on the server, and a duplicate stops it starting. A scaffold whose id is already defined anywhere under server/transforms/ is refused before anything is written.

  • local is reserved for unpublished transforms.

  • An existing service is never overwritten without --force, which replaces the whole service directory and its project.py imports — hand edits and other --name endpoints included. It refuses a service the scaffolder did not write (no marker line in api.py).

  • remove-scaffold <service> deletes a scaffolded service directory and its imports. Never delete the directory by hand: the imports stay and the server no longer starts. It refuses a service whose api.py lacks the line Generated by transformatron scaffold. — a hand-written one, such as the bundled samples. Remove those by hand: the directory and its imports in project.py.

  • samples.json maps each transform id to its sample input. The scaffold prints it, and the smoke test uses it.

Treat the result as a first draft. Expect the live API to disagree with the sample or the spec about which fields are present, what a 404 means, and how errors are shaped. Run the transform on the sample input, read the entities, and correct RECORDS — the loop above exists for exactly this.

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: one fails silently — the code looks right, the run reports success, and no entities come back — and one teaches a method the SDK does not have. docs/transform-authoring.md corrects those, says what never to do in a transform (block), then routes to the SDK guidance for everything else.

Need

Use

Generate a starting point from an API spec

scaffold, then correct it against a live run

Write or change transform code

docs/transform-authoring.md, then the SDK skills

Run, reload, or inspect the server

The CLI or the MCP tools

The loop

  1. scaffold --dry-run from a spec and read the field inventory and Notes; fix it with --records/--map until it maps what matters, then scaffold for real. Or add or edit a module under server/transforms/ by hand.

  2. Import it in server/project.py (from transforms.my_module import *); scaffold does this for you. The server only discovers what project.py imports — this is the most common reason a new transform never shows up. (server/transforms/local/ is the exception; see below.)

  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 on the sample input — and read the entity count, the distinct count, the message levels and the properties, not the state.

  5. Correct the mapping — edit RECORDS, or re-scaffold with --force — and go round again. Finish with the smoke test.

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.

Purpose

CLI

MCP tool

Start the server

start [--ssl]

server_start(ssl=False)

Stop it

stop

server_stop()

Reload after a code change

restart [--ssl|--no-ssl]

server_restart()

Running, healthy, transform count

status

server_status()

Recent log output

logs [--lines N]

server_logs(lines=50)

Advertised transforms and their types

list

list_transforms()

Detail document for one transform

show <id>

get_transform(id)

Advertised entity types

entities

list_entities()

Run one transform

run <id> <type> <value> [--json]

run_transform(..., full=False)

Seed URL and registration steps

seed-url

get_seed_url()

Self-signed cert for HTTPS

certs [--force]

generate_certs(force=False)

Scaffold from cURL / OpenAPI

scaffold --curl ...|--openapi ... [--sample-response JSON] [--sample-input VALUE] [--operation ID] [--name NAME] [--records PATH] [--map PATH=TYPE] [--primary PATH] [--dry-run] [--force]

scaffold_transform(..., sample_input=None, primary=None, dry_run=False)

List a spec's operations

scaffold --openapi ... --list-operations [--service S]

list_operations(openapi, service=None)

Undo a scaffold

remove-scaffold <service>

remove_scaffold(service)

run prints one line per entity — type value {property=value, ...} [link label] — under Entities (N, M distinct):, leaving out blank properties (None, [], empty or whitespace text, such as the SDK's default Port property), with each message prefixed by its level ([INFO], [WARNING], [ERROR], [DEBUG]). A success state with zero entities and an [ERROR] message adds Warning: the transform logged an error and returned nothing; the success state does not mean it worked. under the state. A run that ends FAILED also shows the traceback the server logged during it. --json (full=True) prints every entity and link as the full JSON the server sent. run or show with an id the server does not know prints the 404 with how to find the right one: list, the <namespace>.<id> form when the id has no dot, and a restart for a transform added since the server started.

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.

The CLI exits 0 on success and 1 when the operation failed: a start, stop, restart or certificate generation that failed, a server it could not reach (so status exits 1 when nothing is serving, and status || start works), a run that did not end in a success state, a refused scaffold or removal, or an unusable transformatron.toml (reported as one error: line on stderr, without a traceback). Two outcomes do not count as failure: a run that completes with zero entities exits 0, and so does one that completes having logged an [ERROR] — the warning line above does not change that. The output is the signal — for a gate that fails on it, use the smoke test.

The MCP tools read transformatron.toml on every call, as the CLI does, so an edit applies on the next tool call with no MCP restart. A malformed file comes back as the tool's result rather than stopping the MCP server loading. An unexpected crash inside a tool reaches the agent as Error executing tool <name>: <ExceptionType>: <message>; its traceback is on the MCP server's stderr.

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. In both, an exported shell variable beats the file, and for the smoke test 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.

Both shapes are picked up: a package (local/<service>/ with an __init__.py), from which every module except api.py is imported, and a single-file module directly under local/ (local/lookup.py). Names starting with _ are skipped at either level, so helpers can sit alongside. The server log records what was loaded at startup — Local transforms loaded: ... — so check logs when a local transform is missing from list.

It is discovered rather than imported by name, because a clone without the private modules must still boot, 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:

Agent

Entry point

Claude Code

.claude/skills/maltego-transform-author/, which ships with the clone

Codex, Gemini CLI, others

AGENTS.md, per the AGENTS.md convention

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/, nor transformatron.toml, which the tools read on every call.

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.

Example

Shows

Key

server/transforms/rdap/

Registration data, no API key — pivotable output, redirect handling

no

server/transforms/examples/ffraud.py

Single module, no API key — the minimal shape

no

server/transforms/ransomwarelive/

Multi-module, shared client, upstream field drift

yes

server/transforms/greynoise/

404 as a verdict, silent key acceptance, tight quota

yes

server/transforms/ipinfo/

Bearer auth, one response fanned out to several entities

yes

server/transforms/crowdsec/

Minimal authenticated lookup

yes

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.

RDAP

Against RDAP, the IETF protocol that replaced WHOIS. Registries serve it themselves, so the data is authoritative rather than scraped, and it needs no API key or registration — these run on a fresh clone.

Three transforms, all taking maltego.Domain:

Transform

Returns

RDAP: Domain to Registration

Phrase — registration and expiry dates, notable status, registrar

RDAP: Domain to Nameservers

DNSName — the delegated nameservers

RDAP: Domain to Abuse Contact

EmailAddress, PhoneNumber — the registrar's abuse contacts

Start here if you are learning the shape. It is the closest of the samples to real investigative work: nameservers shared across unrelated domains are a standard way to group infrastructure, and the abuse contact is where a takedown request goes.

Two things it demonstrates that the simpler sample cannot:

  • Following a redirect safely. rdap.org holds no data; it answers with a 302 to whichever registry owns the TLD. The SDK's client sets follow_redirects=False deliberately, because following one silently would send your headers to a host the transform never chose. The hop is taken explicitly, once, and only to an https target.

  • Reading a response off an exception. The client returns only 2xx and raises on everything else, so that 302 arrives as MaltegoHTTPDataProviderInvalidResponse rather than as a response you can inspect. Catching and logging it — the natural thing to write — yields zero entities and still reports success. The redirect target has to come off the exception's response.

Note that example.com is registered through IANA's reserved-name process and publishes no abuse contact, which is why the smoke test pins python.org for that transform.

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:

Transform

Returns

ffraud: IP Reputation

Phrase — fraud score, risk band, detection flags, threat tags

ffraud: IP to Network Details

AS, ISP, DNSName, Location

ffraud: IP to Abuse Contact

EmailAddress from WHOIS

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`).
                 .agents/, project.py and __init__.py are upstream-owned and excluded from
                 ruff so regeneration does not churn; the rest is linted.
  project.py     entrypoint; imports decide what gets registered. Also bounds HTTPS shutdown
  discovery.py   finds and imports the modules under transforms/local/
  transforms/    your transform modules go here — linted like the rest of the project
    local/       gitignored; packages and single modules discovered at startup
.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
  upstream-sdk-shutdown-issue.md  draft report on shutdown stalls, not yet filed
CHANGELOG.md     what changed, per release
tests/           unit tests for the control plane and the scaffolder
transformatron.toml  server name, namespace, author, host, port. Gitignored; copy the .example.
.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 import or a missing annotation. Input type comes from the parameter annotation, output from the return annotation: with no return annotation the output is NONE and the client cannot route it. A bare -> list is worse — the server does not start, and the registration error blames input_entity rather than the return type.

  • 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.

  • entity.add_property does not exist, though the SDK skills teach it. Use set_property(name, value, display_name=...), and assign typed attributes after construction (cve.epss = 0.9), not as constructor arguments.

  • A success state is not proof a transform works. Check the entity count and the message levels, or run scripts/smoke_test_transforms.py. An upstream 403 or 500 caught and logged with context.log.fatal shows as [ERROR] and still ends COMPLETED (success).

  • Never block in a transform — no requests, synchronous httpx, or time.sleep. A blocking call pins the SDK's runner thread, and shutdown waits for it with no limit, so stop and restart give up after 10s and kill the server.

  • When stop has to kill the server, it says why — a transform blocking the runner thread, open client connections, a TLS handshake that never finished — read from the server log since the stop began. Nothing is lost: the server keeps no state a clean shutdown would save. On HTTPS, a connected Maltego Desktop holds keep-alive connections that uvicorn would wait up to 30s for, and a connection that never completes its handshake up to 60s. server/project.py bounds that wait to about 3s (GRACEFUL_SHUTDOWN_TIMEOUT), which logs a harmless ERROR line from uvicorn, Cancel 0 running task(s), timeout graceful shutdown exceeded. It is a workaround until the SDK exposes the setting — see docs/upstream-sdk-shutdown-issue.md. A freshly generated project.py does not have it.

  • 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 unless transformatron.toml sets another. The SDK's own skill scripts default to 8080 — pass --port with your port 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/, server/project.py and server/__init__.py are excluded from linting: the SDK owns them, and regenerating would churn against upstream. The rest of server/ — transforms/, middleware.py, discovery.py — 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/.

The input is --value if given, else the transform's entry in a samples.json beside it (the scaffolder writes one per service), else a TRANSFORM_SAMPLES entry in the script, else the per-entity-type default. A zero-entity run that logged an [ERROR] fails with that message.

Transforms needing credentials take them through repeated --setting KEY=VALUE, or from .env (see Credentials). Five outcomes report SKIP rather than FAIL — a missing credential, an upstream rate limit (429), exhausted credits (402), 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 samples.json or 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

A fresh clone runs under the SDK's placeholder identity — New Maltego Integration, acme.new_maltego_integration, Acme Corp. Change it before publishing anything: the namespace is part of every transform's fully qualified ID, so two servers that share one collide in the same client.

Copy the template and edit it:

cp transformatron.toml.example transformatron.toml
[server]
server_name = "Acme Threat Intel"
namespace   = "acme.threat_intel"
author      = "Acme Corp"

transformatron.toml is gitignored, so your identity does not travel with a fork. The CLI and MCP server read it on every command or tool call and pass it to the transform server as MALTEGO_SERVER_* variables, which outrank the values in server/project.py — so you never edit project.py to rename a server. The same file sets host and port (see transformatron.toml.example). Nothing needs relaunching to pick up an edit except the transform server itself, which takes these values at start. Restart it to apply, then confirm:

uv run python scripts/transformatron_cli.py restart
uv run python scripts/transformatron_cli.py list   # ids now carry your namespace

The values in server/project.py remain as fallbacks for running that file directly.

License

MIT — see LICENSE.

Maltego is a trademark of Maltego Technologies GmbH.

Available Tools

14 tools
generate_certsA

Generate a self-signed certificate for serving over HTTPS.

Args: force: Overwrite an existing certificate pair.

NameRequiredDescriptionDefault
forceNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the 'force' parameter's overwrite behavior, which is useful, but it doesn't state what happens when a certificate pair already exists and force is false, nor where files are written. Some behavioral context is missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: two sentences plus a brief args block. The main purpose is front-loaded, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with one parameter and has an output schema, so the description needn't explain return values. It covers the tool's purpose and the parameter adequately. Minor gaps remain about default behavior when force is false, but overall it is sufficiently complete for a straightforward tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for the 'force' parameter. The description fully compensates by documenting its meaning and effect ('Overwrite an existing certificate pair'), which is the only explanation of this parameter's semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's specific action ('Generate') and resource ('self-signed certificate') with a context ('for serving over HTTPS'). This distinguishes it from sibling server management tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context for when to use (setting up HTTPS serving), but does not explicitly discuss alternatives or when not to use. No alternative certificate tools exist among siblings, so exclusions are unnecessary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_seed_urlA

Return the seed URL and the steps to register this server with Maltego.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description's 'Return' clearly indicates a read-only operation with no side effects. It does not mention authentication or error handling, but for a simple zero-parameter getter this is adequate. The behavioral profile is transparent enough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that conveys the essential information with no unnecessary words. It is well-structured and front-loaded with the key verb and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter getter, the description is fully complete. It states exactly what is returned, and the presence of an output schema handles return value details. No significant context is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the description correctly adds no parameter-specific information. The schema is trivially covered (100%), and the baseline for 0 params is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the seed URL and registration steps. It uses a specific verb 'Return' and identifies the exact resource, distinguishing it from sibling tools focused on transforms, server management, or entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used to retrieve registration information, but it does not explicitly state when to use it relative to siblings. No exclusions or alternative tool references are provided, so the usage context is clear but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_transformA

Return the full detail document for one transform.

Args: transform_id: Fully qualified transform name from list_transforms.

NameRequiredDescriptionDefault
transform_idYes

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It only says it returns a document; it does not disclose error behavior, permissions, or other operation traits. This is a minimal disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences plus an argument line. It is front-loaded, free of unnecessary words, and every sentence provides useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple fetch-by-ID tool with an output schema, the description is mostly complete: it explains what is returned and the parameter. It lacks error/not-found behavior, but given the low complexity this is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter, transform_id, is described as a 'Fully qualified transform name from list_transforms', which adds essential meaning beyond the bare string type in the schema. This fully compensates for the 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Return') and resource ('full detail document for one transform'), and the singular 'one' distinguishes it from sibling list_transforms. This is a specific verb+resource pair that leaves no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'from list_transforms' implies a workflow (call list_transforms first to get the ID), giving some usage context. However, it does not explicitly state when to use this tool versus alternatives or any exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_entitiesA

List the entity types the running server advertises.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of exposing behavior. 'List the entity types the running server advertises' conveys that this is a read-only operation and that results are dynamic based on the server's advertised capabilities, which is useful context beyond simply stating the action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no redundant words. Every word contributes meaning, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and an output schema is present, the description need not explain return values. The statement fully covers the tool's purpose and source of truth ('running server'), making it complete for a list operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so per rubric the baseline is 4. There is nothing to clarify, and the description does not add unnecessary parameter-related noise.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('List') and a precise resource ('entity types') scoped to the running server. This clearly distinguishes it from sibling tools like list_transforms, as 'entity types' is a distinct concept.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used to discover available entity types on the current server, but provides no explicit guidance on when to use it versus alternatives like list_transforms. For a simple zero-parameter list operation, the implied usage is adequate but lacks direct comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_operationsA

List the operations in an OpenAPI spec and what scaffolding each would produce.

One row per operation: HTTP method, path, operation id (pass it to scaffold_transform's operation_ids), the transform id it would get, the inferred input entity, whether the spec has a response schema to map outputs from, and why an operation would be skipped. Nothing is written.

Args: openapi: An OpenAPI/Swagger spec in JSON or YAML: a URL, a file path, or the document itself (as text or as an object). service: The service name you will scaffold under, so the transform ids shown match the ones scaffold_transform generates.

NameRequiredDescriptionDefault
openapiYes
serviceNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and does well: it discloses that it is read-only ('Nothing is written'), enumerates exactly what each row contains, and explains inference behavior (inferred input entity, skip reasons). It stops short of covering auth needs, rate limits, or error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then a dense but informative column list, then an Args section. The long enumeration of row columns is justified since it defines the output contract, though it is slightly verbose given an output schema exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Both parameters are explained despite 0% schema coverage, the read-only behavior is stated, and an output schema exists so return structure need not be re-described (though the row list does so anyway). Missing only edge-case/error handling, which is minor.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: 'openapi' documents accepted forms (URL, file path, or the document as text/object), and 'service' explains its purpose (making transform ids match scaffold_transform's output). It omits format edge cases but adds real meaning beyond the bare anyOf schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (List) and resource (operations in an OpenAPI spec) plus the value-add (what scaffolding each would produce). It is clearly distinguishable from siblings like list_transforms, list_entities, and get_transform, which operate on different resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly positions this as a pre-scaffolding inspection step by telling the agent to pass the operation id to scaffold_transform's operation_ids, which gives a clear workflow context. It lacks an explicit when-not-to-use statement or contrast against list_transforms.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_transformsA

List every transform the running server advertises, with input and output types.

An output type of NONE means the transform function is missing a return annotation, which stops the Maltego client from routing to it.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the responsibility. It discloses a notable behavioral trait: an output type of NONE indicates a missing return annotation, which prevents Maltego client routing. This adds valuable context beyond a generic list and helps interpret results. Side effects aren't mentioned, but as a read-only listing operation, this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first front-loads the main purpose, and the second adds a specific caveat about the NONE type. Both sentences are informative with no unnecessary words, making it highly concise and well structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (no params, output schema present), the description is complete. It states what is listed, includes the NONE-type explanation which aids output interpretation, and mentions the 'running server' context. No important details are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline score of 4 applies. The description mentions input and output types, giving the user an expectation of what the tool reports without needing parameter definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists every advertised transform with input and output types, using a specific verb ('list') and resource ('transforms'). It distinguishes from siblings like get_transform (single transform) and list_entities (different entity type), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context: discovering available transforms on the running server. It doesn't explicitly name alternatives or state when not to use it, but the purpose is self-evident and the tool is simple enough that no further exclusions are needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

remove_scaffoldA

Delete a scaffolded service directory and its imports in project.py.

Use this to undo a scaffold. Deleting server/transforms// by hand leaves its imports behind, and the server then fails to start. Restart the server afterwards. Only services the scaffolder generated are removed: a hand-written service (its api.py lacks the scaffold's generated marker) is refused, and the output says so.

Args: service: Service name slug, the directory name under server/transforms/.

NameRequiredDescriptionDefault
serviceYes

Output Schema

NameRequiredDescription
resultYes

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so: it discloses the destructive scope (only scaffolder-generated services), the mutation of project.py imports, the failure mode of the naive approach, the refusal condition for hand-written services indicated by the missing generated marker, and the post-condition that the server must be restarted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences front-load the action and the undo rationale before the caveats, and the Args block is minimal. No sentence is filler; each conveys a distinct constraint or consequence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, return values need not be explained, and the description still notes that refusals are reported. Safety, side effects, refusal conditions, and required follow-up are all covered for a single-parameter destructive tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it does: 'Service name slug, the directory name under server/transforms/' tells the agent the exact format (slug) and the filesystem location the value must match, which the bare schema type 'string' does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource ('Delete a scaffolded service directory and its imports in project.py') and names the exact artifact affected. It is clearly distinguished from the sibling scaffold_transform (create) and from list/get transforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use it ('Use this to undo a scaffold') and contrasts it with the tempting alternative (deleting the directory by hand), explaining that manual deletion leaves imports behind and breaks server startup. It also names the required follow-up action (restart the server).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

run_transformA

Run a transform against one input entity and return its results.

The output starts with the run state, then each status message prefixed with its severity ([INFO], [WARNING], [ERROR], [DEBUG]), then the entity count and the number of distinct (type, value) pairs, one line per entity: type value {property=value, ...} [link label], and the link count. A run that FAILED also shows the last Python traceback from the server log.

Check the entity count and the message severities: a transform can report success while producing nothing, and an upstream 403 or 500 logged as [ERROR] still ends the run in a success state; a run like that with no entities gets a Warning line under the state. An unknown transform id comes back with how to find the right one. See docs/transform-authoring.md.

Args: transform_id: Fully qualified transform name from list_transforms. entity_type: Maltego type of the input entity, e.g. maltego.Domain. entity_value: Value carried by the input entity. settings: Optional transform settings, keyed by setting name. timeout: Seconds to wait before cancelling the run. full: Return every entity and link as the full JSON the server sent instead of one line each. Use it when a property, display field, or overlay that the summary cuts short matters.

NameRequiredDescriptionDefault
fullNo
timeoutNo
settingsNo
entity_typeYes
entity_valueYes
transform_idYes

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so unusually well: it warns that a transform can report success while producing nothing, that an upstream 403/500 logged as [ERROR] still ends the run in a success state, that such runs get a Warning line under the state, that FAILED runs include the last Python traceback, and that an unknown transform id returns remediation guidance. These are non-obvious runtime behaviors an agent could not infer from the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose sentence, then organized into output format, operational warnings, and an Args block — well structured with no filler prose. The multi-paragraph output-format walkthrough is heavier than strictly needed given an output schema exists, which is the only inefficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a six-parameter tool with zero annotation coverage, the description covers everything an agent needs: all argument semantics, the exact output layout, failure-mode interpretation, and where to look for the transform id. Nothing material to correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it documents all six parameters with meaning beyond their titles: transform_id must be fully qualified and comes from list_transforms, entity_type is a Maltego type with an example, entity_value is the carried value, settings are keyed by setting name, timeout is seconds before cancellation, and full returns raw server JSON with a stated rationale.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Run a transform against one input entity and return its results.' An agent can distinguish it from list_transforms/get_transform/scaffold_transform purely from the name plus this sentence, since those retrieve or create while this executes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a workflow pointer ('Fully qualified transform name from list_transforms') and explicit guidance for one parameter ('Use it when a property, display field, or overlay that the summary cuts short matters'), which is real when-to-use advice. However it never states when NOT to invoke it or names an alternative execution path, so the tool-selection guidance remains implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

scaffold_transformA

Scaffold a new Maltego transform module from a cURL command or OpenAPI specification.

The output lists each transform by the id the server registers it under, its sample input, a ready-made run command, and a field inventory: every leaf path seen in the sample response or schema, its sample value, and what happened to it (mapped to an entity, attached as a property, or dropped and why). Read the inventory and the notes: data an investigator needs is often nested deeper than the scaffolder follows by default. Preview with dry_run=True, adjust records/mappings/primary until the inventory maps what matters, then call again without dry_run. Restart the server, then call run_transform with the transform id exactly as listed (it includes the namespace).

Args: service: Optional service name slug (e.g. 'greynoise', 'threatfox'). curl: A full cURL command string demonstrating an API request. openapi: An OpenAPI/Swagger spec in JSON or YAML: a URL, a file path, or the document itself (as text or as an object). Use list_operations first to pick operation_ids. sample_response: A real JSON response (as text or as the parsed value), to map outputs from. Works with curl, and with openapi when exactly one operation is scaffolded (name it in operation_ids); without it a spec with no response schema maps nothing. operation_ids: OpenAPI operation ids to scaffold. Without it every GET operation is scaffolded; POST operations are only scaffolded when named here. force: Replace an existing service: its whole directory and its project.py imports. name: Transform name for a curl scaffold (default '_lookup'), so one service can grow endpoint by endpoint. records: Dotted path to the list of records in the response, e.g. 'data' or 'containers.cna.references'. Each element becomes one record to map. mappings: Per-field overrides as 'dotted.path[].field=EntityType', e.g. 'containers.cna.affected[].vendor=Company'. EntityType 'Property' attaches the field to the record's entity as a property; 'Drop' ignores it. dry_run: Parse and report the plan (transforms, field inventory, notes, auth, and the files that would be written) without writing anything. sample_input: An input value the API answers for, e.g. the hash or id the sample_response came from. The generated transform skips the input when the response echoes it back (so the other fields enrich the input instead of a copy of it), and samples.json records it so the smoke test sends a value the endpoint accepts. A curl scaffold defaults to the value in the request; give it for an openapi scaffold, which otherwise only has the spec's example, if any. primary: Path of the field to use as each record's primary entity, the one every other field attaches to as a property, written like a mappings path (e.g. 'SHA-1' or 'hits.hits[]._id'). It must map to an entity type; the error names the valid candidates.

NameRequiredDescriptionDefault
curlNo
nameNo
forceNo
dry_runNo
openapiNo
primaryNo
recordsNo
serviceNo
mappingsNo
sample_inputNo
operation_idsNo
sample_responseNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does so well: force is disclosed as destroying 'its whole directory and its project.py imports', dry_run is described as writing nothing but reporting 'the files that would be written', and the generated transform's echo-skipping and error behavior for primary are explained. It stops short of stating permission or rate-limit requirements outright, though it notes auth is surfaced in the dry-run report.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then workflow, then a per-argument glossary, which is the right ordering for a 12-parameter tool. The opening paragraph on the output inventory is somewhat long given an output schema exists, but it explains the decision-critical inventory semantics, so it mostly earns its space.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 12-parameter, zero-required, no-annotation scaffold tool, the description supplies the full selection workflow, the side effects of force and dry_run, the sequence to run_transform, and complete per-parameter semantics. An agent could drive this tool end-to-end from the description alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate entirely, and it does: every one of the 12 parameters is documented with type, default, and format examples (e.g. records as a dotted path, mappings as 'dotted.path[].field=EntityType', operation_ids defaulting to all GETs). This is far more than the bare-typed schema conveys.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Scaffold a new Maltego transform module') plus the two concrete input sources (cURL command or OpenAPI spec). It is immediately distinguishable from siblings like run_transform, list_operations, and remove_scaffold, going so far as to name list_operations as a prerequisite and run_transform as the follow-up step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit workflow ('Preview with dry_run=True, adjust records/mappings/primary until the inventory maps what matters, then call again without dry_run. Restart the server, then call run_transform...'). It also states selection conditions per input mode: 'Use list_operations first to pick operation_ids', operation_ids needed for POST, sample_response needed for openapi with no response schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

server_logsA

Return recent server log output.

Args: lines: Number of trailing log lines to return.

NameRequiredDescriptionDefault
linesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It only says 'return' logs, not whether this is safe/read-only, whether auth is needed, or how recent is defined. No caveats or side effects are disclosed, making it insufficiently transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences, purpose first, then parameter explanation. No wasted words, and the structure is front-loaded with the primary function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with an output schema, the description is adequate but not fully complete. It lacks usage guidance and behavioral transparency (e.g., whether the server must be running, what 'recent' means, output format beyond schema). It's a minimal viable description, not a rich one.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero description coverage, but the description adds 'Number of trailing log lines to return,' giving meaning to the 'lines' parameter. This directly compensates for the schema gap, though it's the only parameter and the added detail is minimal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Return recent server log output,' using a specific verb and resource. It distinguishes from sibling tools like server_start, server_stop, server_status, which are about control/status, not logs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use this tool to fetch server logs. However, there's no explicit 'when to use' or mention of alternatives. No exclusions or conditions are stated, so it's not fully transparent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

server_restartA

Restart the server to pick up new or edited transform modules.

Args: ssl: Serve over HTTPS after restarting. Left unset, the scheme the server is already running under is preserved, so reloading an HTTPS server keeps the Maltego desktop client working.

NameRequiredDescriptionDefault
sslNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It usefully discloses the non-obvious ssl behavior (preserving the current scheme), but it does not mention potential downtime or other side effects of a restart. This goes beyond a bare statement but lacks rich detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact: one sentence defining purpose and a brief parameter explanation. No padding, every sentence contributes to understanding the tool and its single argument.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one optional parameter, the description covers the purpose and parameter semantics effectively. Since an output schema exists, return values need not be described in the text. Minor omissions like downtime impact are acceptable given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description's 'Args' section fully compensates by explaining the ssl parameter in detail: it can enable HTTPS, and when left unset it preserves the existing scheme. This adds substantial meaning beyond the schema's bare type definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Restart the server to pick up new or edited transform modules,' clearly stating the verb, resource, and purpose. This distinguishes server_restart from sibling tools like server_start, server_stop, or server_status, which have different actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'to pick up new or edited transform modules' provides clear context for when to use this tool. It does not explicitly name alternatives or exclusions, but the purpose is clear enough to differentiate it from related server operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

server_startA

Start the local transform server and wait until it answers.

Args: ssl: Serve over HTTPS. Requires generate_certs first. Both the Maltego desktop client and the Graph Browser require it.

NameRequiredDescriptionDefault
sslNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses a meaningful behavioral trait: the tool blocks until the server answers. It also discloses the dependency on generate_certs for SSL mode, adding useful operational context beyond a trivial 'start' statement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the primary purpose, followed by a concise Args block. Every sentence and clause adds value—there is no fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one optional parameter and an output schema, the description is complete enough. It covers the core behavior, the parameter meaning, and a prerequisite. The output format is not explained, but the presence of an output schema makes that unnecessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only provides the parameter name and type, with 0% description coverage. The description's Args section fully explains ssl: it means 'Serve over HTTPS', requires generate_certs first, and is necessary for the Maltego desktop client and Graph Browser. This adds complete semantic meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Start the local transform server') and even adds the readiness condition ('wait until it answers'). This distinguishes it from sibling tools like server_stop, server_restart, and server_status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives specific usage context for the ssl parameter: it requires generate_certs first, and both Maltego desktop client and Graph Browser require HTTPS. However, it does not explicitly state when to use server_start instead of server_restart or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

server_statusA

Report whether the server is running, healthy, and how many transforms it serves.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of conveying behavioral traits. The word 'report' clearly indicates a read-only operation, and the explicit listing of what is reported (running, healthy, transform count) gives the agent concrete behavioral expectations. It does not discuss side effects, auth, or errors, but for a simple status query this level is reasonably complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly written sentence that front-loads the verb and immediately specifies the three outputs of interest. Every word contributes meaning; there is no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, status-reporting tool with an output schema, the description is complete. It names all the reported aspects (running, health, transform count) without needing to explain return values, as those are covered by the output schema. The simplicity of the operation does not require further elaboration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description adds no parameter-level details, but none are needed since there is nothing to configure. It correctly focuses on the output dimensions instead.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'report' paired with the resource 'server' and clearly enumerates the exact outputs (running, healthy, transform count). This fully distinguishes it from sibling tools like server_start, server_stop, and server_logs, which are also self-descriptive in their names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage as a health/status check before or during server operations, but it does not explicitly state when to use it versus alternatives. There is no mention of exclusions or alternative tools, though the context of sibling tool names provides implicit differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

server_stopB

Stop the running transform server.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. 'Stop the running transform server' conveys only the action itself, with no info about side effects (e.g., whether running transforms are interrupted, if shutdown is graceful or forced, or if the operation is reversible). This is a significant gap for a state-changing operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no fluff or repetition. It is front-loaded with the verb and resource, maximizing information density.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (no params, likely void output), so minimal description might suffice. However, given no annotations, the description omits critical behavioral context—what happens to ongoing work, whether the server must be running, and error conditions. It is adequate but leaves gaps, so it does not earn a higher score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, and schema coverage is 100% (vacuously). The description adds no parameter semantics because none exist. Since 0 params has a baseline of 4 per rubric, this score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Stop' and a clear resource 'the running transform server', making it obvious what the tool does. It also distinguishes itself from sibling tools like server_start, server_restart, and server_status by naming a distinct action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. It does not mention prerequisites (server must be running), complementary tools (server_start), or scenarios where stop vs. restart or stop vs. status is appropriate. The usage context is only implied by the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updates
    • Addedlist_operations
    • Addedremove_scaffold
    • Changedrun_transform1 field changed
      • addedInput schema / properties / full
        Added value: +{
+  "default": false,
+  "title": "Full",
+  "type": "boolean"
+}
    • Changedscaffold_transform10 fields changed
      • addedInput schema / properties / dry_run
        Added value: +{
+  "default": false,
+  "title": "Dry Run",
+  "type": "boolean"
+}
      • addedInput schema / properties / force
        Added value: +{
+  "default": false,
+  "title": "Force",
+  "type": "boolean"
+}
      • addedInput schema / properties / mappings
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Mappings"
+}
      • addedInput schema / properties / name
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Name"
+}
      • changedInput schema / properties / openapi / anyOf
        Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]
      • addedInput schema / properties / operation_ids
        Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Operation Ids"
+}
      • addedInput schema / properties / primary
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Primary"
+}
      • addedInput schema / properties / records
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Records"
+}
      • addedInput schema / properties / sample_input
        Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Sample Input"
+}
      • changedInput schema / properties / sample_response / anyOf
        Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "items": {},
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
  2. 12 tool updatesv0.1.0
    • First observedgenerate_certs
    • First observedget_seed_url
    • First observedget_transform
    • First observedlist_entities
    • First observedlist_transforms
    • First observedrun_transform
    • First observedscaffold_transform
    • First observedserver_logs
    • First observedserver_restart
    • First observedserver_start
    • First observedserver_status
    • First observedserver_stop
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A4.2/5.0

Scored across 14 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: server lifecycle (start/stop/restart/status/logs), transform execution (run_transform), scaffolding (scaffold_transform, list_operations, remove_scaffold), listing (list_transforms, list_entities), and configuration (generate_certs, get_seed_url). No two tools overlap in a way that would cause misselection. Descriptions reinforce the boundaries.

Naming Consistency4/5

All names use snake_case, and most follow a verb_noun pattern (list_transforms, run_transform, get_transform). The server lifecycle tools use noun_verb (server_start, server_stop), which is a minor deviation but still predictable and grouped. No chaotic mixing.

Tool Count5/5

14 tools is well within the ideal range for this domain. Each tool earns its place, covering distinct operations without redundancy. The set is neither too thin nor too heavy.

Completeness5/5

The surface covers server lifecycle, transform listing and execution, scaffolding from APIs, removal of scaffolded services, entity listing, certificate generation, and seed URL setup. CRUD-like coverage for transforms is present (create via scaffold, read via list/get, run, delete via remove_scaffold for generated ones). Minor gaps like editing hand-written transforms are acceptable given the scaffold-centric workflow.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

Related MCP Connectors

Related MCP Servers