Model Context Protocol (MCP) server for Apollo.io API integration
Nothing to run from here
AI Tooling Detector is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
96/100
trust score
9
downloads a day
local
runs on your machine
289
downloads in 30d
What it does
Detect whether a company declares, deploys, or charges for AI, with evidence.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --env 'APIFY_TOKEN=${APIFY_TOKEN}' --transport stdio ai-tooling-detector-mcp '--' npx -y @mambalabsdev/mcp-ai-tooling-detector
# 2 — ask your agent for something these tools do
# detect_ai_tooling
It needs APIFY_TOKEN: this reads it from your environment, so export it first.
AI Tooling Detector costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publisher
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts AI Tooling Detector as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. Claude Code reads APIFY_TOKEN (Apify API token from https://console.apify.com/account/integrations) from your environment each time it starts the server, so the value stays out of its config: export it in your shell profile (macOS, Linux), or run setx APIFY_TOKEN … and open a new terminal (Windows). The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user --env 'APIFY_TOKEN=${APIFY_TOKEN}' --transport stdio ai-tooling-detector-mcp '--' npx -y @mambalabsdev/mcp-ai-tooling-detectorReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
It reaches a system of yours, so it needs your own credential rather than the publisher’s: APIFY_TOKEN — Apify API token from https://console.apify.com/account/integrations. You set it in your own client’s config, on your machine — this marketplace never holds it.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
1 tool, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Given a company domain, determine how far that company has actually gone with AI. Returns an ai_maturity tier of none, declared (says AI but nothing observable is running), deployed (AI tooling is live on the site), or commercialized (the pricing page charges for AI via credits, tokens, an add-on,…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| domain | string | no | A single company domain, e.g. intercom.com. Provide either domain or domains. |
| domains | array | no | Batch mode: several company domains analyzed in one call. Takes precedence over domain. |
| vendors | array | no | Report only these AI vendors, which turns the actor into competitive intelligence for any one of the 52 fingerprinted tools. Detection is unchanged either way, so a filtered call costs the same. The s |
| check_pricing | boolean | no | Fetch and score the pricing page. Default true. Setting this false is faster but caps the result at 'deployed', because 'commercialized' can only be proven on a pricing page. |
| skipCache | boolean | no | Force a fresh analysis and ignore the 7 day result cache. |
| request_timeout_ms | integer | no | Per-request timeout in milliseconds. 3000 to 20000. Default: 9000. |
| batchSize | integer | no | Batch mode only: how many domains the actor analyzes at once. 1 to 10. Default: 5. |
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install AI Tooling Detector or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 96 / 100
Release 1.3.1 · scanned 14 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score96
Findings1 threat · 1 capability
Capabilitymoderate
Coveragesource
Verificationrepo
Surface digest4c01490ad6e33b43132f79f942577b699ee2e8a266d27edb00afff112b9a95b2
Score vectorthreat 100 → client 96
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | moderate | capability blast radius (moderate) — client exposure if the model is manipulated | −3 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (1 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. moderate is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| detect_ai_tooling | untrusted-inputexternal-sink | untrusted-input: declares openWorldHint: true (interacts with entities outside the server); external-sink: handler sends an HTTP POST (fetch(https://api.apify.com/v2/acts/${actorPath}/runs?timeout=${ACTOR_RUN_TIMEOUT_SECS}) at build/index.js:80 |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
flow-1
Untrusted input can drive an external action even though no sensitive source is exposed.
Supply chain and provenance
Integrityfirst-seen
Surface digest4c01490ad6e33b43
Scanned2026-10-14
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 1.3.1. Pin a release below and the command asks for exactly that one.
1.3.1
6 Oct 2026
latestNo release note was published with this version.
1.2.0
undated
- The version this listing was on when mcprush began following its releases. The registry was not asked when it shipped, so this row carries no date.
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