Borrowing it
Nothing to install: this file belongs to khadinakbarlabs/ai-brand-monitor-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/khadinakbarlabs/ai-brand-monitor-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/khadinakbarlabs/ai-brand-monitor-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/khadinakbarlabs/ai-brand-monitor-mcp/claude-md)<a href="https://agentmods.dev/instructions/khadinakbarlabs/ai-brand-monitor-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/khadinakbarlabs/ai-brand-monitor-mcp/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/khadinakbarlabs/ai-brand-monitor-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/khadinakbarlabs/ai-brand-monitor-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01254 | $0.01254 |
| Opus 5 | $0.00627 | $0.00627 |
| Sonnet 5 | $0.00251 | $0.00251 |
| Haiku 4.5 | $0.00125 | $0.00125 |
Grade A, and why
ai-brand-monitor-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-brand-monitor-mcp
What this is
Standalone Node.js MCP server that wraps the khadinakbar/ai-search-brand-monitor Apify actor. Exposes two tools (audit_brand_visibility, check_brand_in_queries) so any MCP-compatible AI agent can measure brand visibility across Perplexity, ChatGPT, Claude and Gemini.
Relationship to the Apify actor
- Upstream source of truth: the Apify actor at apify.com/khadinakbar/ai-search-brand-monitor. This MCP package only calls it via
apify-client. - MCP also available without this package — users can hit
https://mcp.apify.com?tools=khadinakbar/ai-search-brand-monitordirectly. This repo adds npm-distributable / Smithery-distributable alternatives + branded tool names (audit_brand_visibilityvs Apify's defaultai-search-brand-monitor) for better agent ergonomics.
Template pattern
This project is a direct copy of the google-maps-scraper-mcp structure — same folder layout, same smithery.yaml shape, same server.json schema, same src/{index,server,config,apify,types}.ts + src/tools/*.ts pattern. When updating one, consider updating the other for consistency.
Architecture
- Transport: stdio (
@modelcontextprotocol/sdk/server/stdio.js) - Actor invocation:
apify-client(client.actor(id).call(input, { waitSecs })) - Dataset projection: trims heavy fields (
ai_response_summary→ 280 chars,mention_context→ 240 chars,cited_urls→ max 8) so 20 items stay well under the 25k token output cap. LAST_RUN_SUMMARYfrom the actor's key-value store is surfaced asresult.summaryfor agent-level GEO score / recommendations.
Tools
audit_brand_visibility
Template-driven GEO audit. Required: brandName. Optional: brandDomain, brandAliases, industry, competitors, platforms, queryTemplates, maxQueriesPerPlatform.
check_brand_in_queries
Custom-question mode. Required: brandName, customQueries (1–10). Optional: brandDomain, brandAliases, competitors, platforms.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 76 lines · 1,254 tokens per session scan A d0296f6e1730
ai-brand-monitor-mcp CLAUDE.md is an instructions file published in the GitHub repository khadinakbarlabs/ai-brand-monitor-mcp (28 stars, last pushed 3mo ago), licensed MIT. It adds 1,254 tokens to every session, about $0.0063 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).