mdowis/anansi — A self-healing web scraper built for hostile sites: selectors repair themselves, browser rendering k…

Description and use

Here's a brief summary of the open-source project mdowis/anansi:

**Summary:** mdowis/anansi is a self-healing web scraper designed for hostile sites, featuring adaptive selectors, browser rendering, and evasion of bot detection through Chrome TLS fingerprinting. It comes with an MCP (Model Context Protocol) server, enabling AI assistants to drive full crawls through conversation. This project standardizes integrations between AI agents and tools, fostering connectivity and automation.

**Value Proposition:** The project's value lies in its ability to bridge the gap between AI assistants and real-world tools and data through a standardized protocol. This enables seamless integration, automation, and scalability, ultimately driving business growth and efficiency.

**Practical Adoption Path:**

1. **Evaluate the project:** Assess the code quality, security posture, and license to ensure it aligns with your organization's standards.
2. **Integrate with AI assistants:** Use the MCP server to connect your AI agents to the web scraper, enabling them to drive full crawls through conversation.
3. **Ship the MCP server:** Deploy the MCP server to standardize integrations between AI agents and tools, ensuring seamless communication and data exchange.
4. **Standardize integrations:** Leverage the MCP protocol to integrate with various tools and services,

Problem

mdowis/anansi helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

A self-healing web scraper built for hostile sites: selectors repair themselves, browser rendering kicks in when needed, and Chrome TLS fingerprinting evades bot detection. Ships with an MCP server so any LLM can drive a full crawl through conversation.

Source