Maigret MCP Server

Overview

The Maigret MCP Server brings the power of the open‑source OSINT tool maigret into AI workflows through the Model Context Protocol. By exposing a set of well‑defined tools, it allows assistants such as Claude Desktop to perform comprehensive username reconnaissance and URL analysis directly from the chat interface. This eliminates the need for developers to manually run command‑line utilities, parse their output, or manage Docker containers—everything is handled transparently by the MCP server.

What problem does it solve?

Investigating digital footprints is a common requirement for security analysts, investigators, and compliance teams. Traditional OSINT workflows involve running multiple scripts, navigating through platform APIs, and collating results into reports. Maigret consolidates these tasks but still requires command‑line interaction and environment setup. The MCP server turns this process into a simple, declarative API call that can be triggered by an AI assistant. Developers no longer need to write adapters or manage tool dependencies; they can focus on higher‑level logic while the server handles execution, caching, and output formatting.

Core capabilities

• Username search across hundreds of sites: A single tool () queries a curated list of social networks, forums, and web services for a given handle. It can limit the search to the top 500 sites or broaden it to all available sources, and supports tag‑based filtering (e.g., photo, dating, US).
• URL parsing and username extraction: The tool dissects a URL, extracts embedded usernames or identifiers, and then performs an automated search for those accounts. This is particularly useful when investigating a link that may reveal the owner’s online presence.
• Multi‑format reporting: Results can be returned in plain text, HTML, PDF, JSON, CSV, or XMind mind‑map format. This flexibility lets developers choose the most suitable format for downstream consumption—whether feeding data into a knowledge graph, generating human‑readable reports, or archiving for compliance.
• Dockerized execution: By running inside a container, the server guarantees consistent behavior across macOS, Linux, and Windows environments. It also simplifies deployment for teams that rely on container orchestration or CI pipelines.

Use cases

• Security & threat intelligence: Quickly map an adversary’s online presence by querying their handle across platforms and aggregating results into a single report.
• Compliance & privacy audits: Verify that no personal data is publicly exposed for a given username, aiding GDPR or CCPA compliance checks.
• Incident response: When an employee’s credentials are compromised, the server can rapidly surface all linked accounts and social media profiles for containment efforts.
• Research & analytics: Academics or journalists can automate large‑scale username investigations, generating structured datasets for analysis.

Integration with AI workflows

Once registered in an MCP‑compatible client, developers can invoke the or tools using simple JSON payloads. The AI assistant can then present the results inline, ask follow‑up questions to refine tags or formats, and even chain multiple calls (e.g., parse a URL, then search the extracted username). Because the server handles execution and output generation, the assistant can focus on conversational context and decision logic rather than infrastructure concerns.

Unique advantages

• Zero‑config AI access: No need to expose API keys or manage authentication; the server runs locally and communicates over a secure IPC channel.
• Extensibility: Developers can add custom site lists or modify the underlying maigret configuration without touching the MCP interface.
• Compliance‑friendly: By restricting searches to publicly available data and providing clear warnings, the server aligns with ethical OSINT practices.
• Cross‑platform reliability: Docker guarantees that the same binaries run on any host, eliminating “works on my machine” problems.

In summary, the Maigret MCP Server transforms a powerful OSINT toolkit into an AI‑friendly service that delivers rapid, multi‑format reconnaissance of usernames and URLs. It streamlines investigative workflows, enhances productivity for developers building AI assistants, and offers a robust, containerized foundation that scales across environments.