EchoBat for AI Agents

EchoBat ships an MCP server, a structured JSON CLI, and token-aware command suffixes so AI coding agents like Claude Code, Cursor, and Windsurf can scan a site, propose fixes, apply them, and re-scan in a single closed loop. Every command is deterministic and pipeable; every response is bounded so it fits comfortably in an agent context window.

Why a Site Crawler Needs an Agent Interface

Generic site-audit tools assume a human will read the output. AI agents need the opposite: deterministic commands, structured JSON, predictable schema, and bounded response sizes that fit inside a context window. EchoBat ships every command with a structured output mode, a token-aware action suffix that trims fields the agent does not need, and an MCP server so the agent can invoke scans the same way it would call any other tool.

A Closed-Loop Scan-Fix-Rescan Workflow

The agent scans the site, the engine returns a structured list of findings with file paths and line ranges where possible, the agent applies fixes to the repository, then triggers a re-scan to verify the findings cleared. This single feedback loop is what most other SEO tools cannot offer because their output is HTML or PDF — not machine-readable findings.