Model Context Protocol (MCP) server for Apollo.io API integration
Nothing to run from here
Contact Classifier 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
15
downloads a day
local
runs on your machine
407
downloads in 30d
What it does
Turn a contact's job title into department and seniority, with an optional check that the person still holds the role.
All of the work runs on Mamba Labs actors on Apify. This server is a thin client: it starts the run, waits for it, and hands back the results. Add it to Claude, Cursor, or any MCP client with one npx line and your own APIFY_TOKEN.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --env 'APIFY_TOKEN=${APIFY_TOKEN}' --transport stdio contact-classifier-mcp '--' npx -y @mambalabsdev/mcp-contact-classifier
# 2 — ask your agent for something these tools do
# classify_contact
It needs APIFY_TOKEN: this reads it from your environment, so export it first.
Contact Classifier costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publisher
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Contact Classifier as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. Claude Code reads APIFY_TOKEN (Apify API token from https://console.apify.com/account/integrations) from your environment each time it starts the server, so the value stays out of its config: export it in your shell profile (macOS, Linux), or run setx APIFY_TOKEN … and open a new terminal (Windows). The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user --env 'APIFY_TOKEN=${APIFY_TOKEN}' --transport stdio contact-classifier-mcp '--' npx -y @mambalabsdev/mcp-contact-classifierReconnect, 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.
Read from its source code. A server that builds its tool list when it starts can list more tools than this.
One contact in, one classified row out. Give it a job title and it returns the department, the seniority level, a seniority_rank from 1 to 12 you can filter with a comparison, and classification_rule, the named rule that fired, so every decision is auditable. The classification is a deterministic…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| job_title | string | yes | The contact's job title, exactly as you hold it. Classified by deterministic rules with no API key needed. |
| full_name | string | no | Only needed for position verification. Classification works without it. This name is never sent to any language model. |
| company_domain | string | no | Only needed for position verification. The company's website domain, with or without https. |
| verify_position | boolean | no | Check whether the person is still listed on their employer's own website. Off by default. Adds roughly 3 seconds and 9 requests per contact, and needs both the name and the domain. Default: false. |
| use_llm_fallback | boolean | no | Off by default. When on, titles the rules cannot place are sent to your own model using the LLM_API_KEY secret environment variable you set on your copy of this Actor. Only the title is sent, never th |
| llm_provider | string | no | Which provider your LLM_API_KEY belongs to. Only read when the LLM fallback is on. Default: "openai". |
| llm_model | string | no | Model id passed straight through to the provider. Only read when the LLM fallback is on. Default: "gpt-4o-mini". |
| skipCache | string | no | Set to true to ignore cached results and classify from scratch. Default: "false". |
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 Contact Classifier 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 1.2.0 · scanned 16 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 · 0 capability
Capabilitymoderate
Coveragesource
Verificationrepo
Surface digest2ea3f4e0725f220c9a384096c36a331465e79a3a3996f4f6e2dc3e8e0d16d7e6
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 |
|---|---|---|
| classify_contact | external-sink | external-sink: declares readOnlyHint: false, openWorldHint: true, 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.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest2ea3f4e0725f220c
Scanned2026-10-16
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 1.2.0. Pin a release below and the command asks for exactly that one.
1.2.0
16 Oct 2026
latestNo release note was published with this version.
1.1.1
6 Oct 2026
No release note was published with this version.
1.0.0
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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