# AIMentionTracker [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/aimentiontracker/mcp  
**GitHub Stars:** 0  
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**Directory Page:** https://allmcps.com/mcp/aimentiontracker

## Description
How often ChatGPT, Claude, Gemini and Perplexity name and cite your brand, with the evidence.

## Tools
Capabilities this server exposes over MCP:

- **get_me** — Who this key is, the plan, and the reporting thresholds. **Call first.
- **get_current_date** — Today in UTC, and the trailing windows every other tool counts back from. **Call before reasoning about dates.
- **list_brands** — Which brands this key can read, and their ids
- **list_questions** — The questions this workspace runs, and how many answers each produced. **Call before reading a disappointing number**: it may be the wrong question, not poor visibility
- **get_visibility** — Presence per engine and per brand over a window
- **get_share_of_voice** — Each tracked brand's mentions as a share of the tracked set
- **get_sentiment** — Recommended, negative and neutral counts per brand, against the classified denominator
- **list_competitors** — Who gets named instead of you, including answers that named them and never mentioned you
- **list_answers** — The stored answers, with mentions and citations
- **list_sources** — Domains the engines read before answering
- **list_alerts** — Findings worth acting on
- **list_recommendations** — What to do next, ranked, with the pages and answers behind each item. **Check `displayable`**: false means the corpus is too thin to rank, not that there is nothing to do

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "aimentiontracker": {
    "command": "npx",
    "args": ["-y","@aimentiontracker/mcp"],
    "env": {
      "AMT_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `AMT_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What AIMentionTracker MCP server does

The AIMentionTracker MCP server lets an MCP client read answer-engine optimisation data for tracked brands. Its measurements are based on answers generated for the workspace’s questions, including whether an answer mentioned or cited a brand. The supported engines are ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.

The data covers brand visibility by engine, share of voice among tracked brands, classified sentiment, competitors named instead of the tracked brand, stored answers, citation details, source domains, alerts, and ranked recommendations. The service is intended for analysis and reporting; the tools are read-only and do not modify billing, team membership, tracked content, or workspace configuration.

## How it works

A workspace API key is scoped to one organisation and determines which brands and questions the client can read. Requests are authenticated with that key and return data from the AIMentionTracker API. The hosted deployment creates a stateless server instance for each request and does not retain secrets, database credentials, vendor credentials, or service accounts.

Tool order matters when interpreting results. Start with `get_me` to identify the key, plan, and reporting thresholds. Call `get_current_date` before reasoning about reporting windows, then use `list_brands` and `list_questions` to confirm the available brands and the question coverage behind a measurement.

Measurements include interpretation safeguards. A null rate indicates that too few answers exist, not zero visibility. Engines are reported separately rather than averaged. Share of voice includes the number of brands counted, while sentiment uses the classified-answer denominator because some mentions cannot be classified reliably. An empty alert result does not necessarily mean that no change occurred; the response explains its meaning.

## Setup and configuration

Create an API key through the AIMentionTracker API-key page. For hosted use, configure an MCP client with the streamable HTTP endpoint at `https://mcp.aimentiontracker.ai` and send the key as a Bearer token in the `Authorization` header.

For local use, run the package with `npx` and provide the key through `AMT_API_KEY`. The documented Claude command registers the local process over stdio. The same key format, beginning with `amt_live_`, is used for authentication in both deployment options.

## Tools and capabilities

The core tool set includes:

- `get_me`, `get_current_date`, `list_brands`, and `list_questions` for workspace context and date-aware analysis.
- `get_visibility`, `get_share_of_voice`, and `get_sentiment` for brand measurements.
- `list_competitors`, `list_answers`, and `list_sources` for evidence, alternatives, citations, and domains.
- `list_alerts` and `list_recommendations` for findings and suggested next actions.

`list_recommendations` includes the pages and answers supporting each item. Check its `displayable` value: false means the available corpus is too small to rank recommendations. `list_search_console` is exposed only when the Google Search Console integration is enabled.

## Limitations and notes

The AIMentionTracker MCP server does not provide write operations in version one. Adding prompts or triggering runs is outside the available tool set. Results also depend on the questions and answer volume configured for the workspace, so a disappointing metric may reflect insufficient or unsuitable question coverage.

AIMentionTracker should be used for answer-engine visibility analysis, not conventional rank tracking. Shares from workspaces with different numbers of tracked brands are not directly comparable, and sentiment counts should be interpreted with their classified denominator. The hosted service forwards the supplied key to the API for authentication, tenancy, entitlement, and rate limiting.

_Full upstream README: https://allmcps.com/mcp/aimentiontracker/readme_