aeollm-readinessweb-auditcontent-extractionmcp
Audit URLs for AI crawler access, structured data, content density, and LLM readiness with scores from 0 to 100.
Quick Install
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Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Overview
Use cases
Audit a website for AI crawler accessibility
Check pages for llms.txt and JSON-LD structured data
Measure content density and LLM extraction quality
Convert web pages into Markdown for agents
Run readiness checks in CI pipelines
Key features
Scores LLM readiness from 0 to 100
Checks 13 AI crawler user agents in robots.txt
Detects llms.txt and llms-full.txt
Evaluates Schema.org JSON-LD data
Converts URLs to token-efficient Markdown
Exposes eight MCP tools through FastMCP
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Aeo Cli.
Extracted Tool Capabilities
Scores LLM readiness from 0 to 100
Checks 13 AI crawler user agents in robots.txt
Detects llms.txt and llms-full.txt
Evaluates Schema.org JSON-LD data
Converts URLs to token-efficient Markdown
Exposes eight MCP tools through FastMCP
Documentation Overview
Context CLI
Lint any URL for LLM readiness. Get a 0-100 score for token efficiency, RAG readiness, agent compatibility, and LLM extraction quality.
Table of Contents
- What is Context CLI?
- Features
- Installation
- Quick Start
- CLI Usage
- MCP Integration
- Context Compiler (Generate)
- Serve Modes
- Citation Radar
- Share-of-Recommendation Benchmark
- GitHub Action
- Score Breakdown
- AI Bots Checked
- Development
- License
What is Context CLI?
Context CLI is an LLM Readiness Linter that checks how well a URL is structured for AI consumption. As LLM-powered search engines, RAG pipelines, and AI agents become primary consumers of web content, your pages need to be optimized for token efficiency, structured data extraction, agent interoperability, and machine-readable formatting.
Context CLI analyzes your content across five pillars (V3 scoring) and returns a structured score from 0 to 100.
Features
- Robots.txt AI bot access -- checks 13 AI crawlers (GPTBot, ClaudeBot, DeepSeek-AI, Grok, and more)
- llms.txt & llms-full.txt -- detects both standard and extended LLM instruction files
- Schema.org JSON-LD -- extracts and evaluates structured data with high-value type weighting (Product, Article, FAQ, HowTo)
- Content density -- measures useful content vs. boilerplate with readability scoring, heading structure analysis, and answer-first detection
- Agent Readiness (V3) -- 20-point pillar checking AGENTS.md,
Accept: text/markdown, MCP endpoints, semantic HTML, x402 payment signaling, and NLWeb support - Markdown-for-Agents engine -- convert any URL to clean, token-efficient markdown; open-source alternative to Cloudflare's Markdown for Agents
- Serve modes -- reverse proxy, ASGI middleware, and WSGI middleware that serve markdown to agents via
Accept: text/markdown - Batch mode -- lint multiple URLs from a file with
--fileand configurable--concurrency - Custom bot list -- override default bots with
--botsfor targeted checks - Verbose output -- detailed per-pillar breakdown with scoring explanations and recommendations
- Rich CLI output -- formatted tables and scores via Rich
- JSON / CSV / Markdown output -- machine-readable results for pipelines
- MCP server -- expose the linter as a tool for AI agents via FastMCP (8 tools including agent readiness, markdown conversion, and AGENTS.md generation)
- Context Compiler -- LLM-powered
llms.txt,schema.jsonld, andAGENTS.mdgeneration, with batch mode for multiple URLs - Web server config generation -- generate nginx, Apache, and Caddy configs for
Accept: text/markdownrouting - x402 payment config generation -- generate payment signaling configuration for monetizing agent access
- CI/CD integration --
--fail-underthreshold,--fail-on-blocked-bots, per-pillar thresholds, baseline regression detection, GitHub Step Summary - GitHub Action -- composite action for CI pipelines with baseline support
- Citation Radar -- query AI models to see what they cite and recommend, with brand tracking and domain classification
- Share-of-Recommendation Benchmark -- track how often AI models mention and recommend your brand vs competitors, with LLM-as-judge analysis
Installation
Context CLI uses a headless browser for content extraction. After installing, run:
Development install
Docker
Build and run Context CLI in a container with all dependencies pre-installed:
Lint a URL:
Pass additional flags as normal:
Quick Start
This runs a full lint and prints a Rich-formatted report with your LLM readiness score.
CLI Usage
Single Page Lint
Lint only the specified URL (skip multi-page discovery):
Multi-Page Site Lint (default)
Discover pages via sitemap/spider and lint up to 10 pages:
Limit Pages
JSON Output
Get structured JSON for CI pipelines, dashboards, or scripting:
CSV / Markdown Output
Verbose Mode
Show detailed per-pillar breakdown with scoring explanations:
Timeout
Set the HTTP timeout (default: 15 seconds):
Custom Bot List
Override the default 13 bots with a custom list:
Batch Mode
Lint multiple URLs from a file (one URL per line, .txt or .csv):
CI Mode
Fail the build if the score is below a threshold:
Fail if any AI bot is blocked:
Per-Pillar Thresholds
Gate CI on individual pillar scores:
Available: --robots-min, --schema-min, --content-min, --llms-min, --overall-min.
Baseline Regression Detection
Save a baseline and detect score regressions in future lints:
Exit codes: 0 = pass, 1 = score below threshold or regression detected, 2 = bots blocked.
When running in GitHub Actions, a markdown summary is automatically written to $GITHUB_STEP_SUMMARY.
Quiet Mode
Suppress output, exit code 0 if score >= 50, 1 otherwise:
Use --fail-under with --quiet to override the default threshold:
Markdown Conversion
Convert any URL to clean, token-efficient markdown optimized for LLM consumption:
Show token reduction statistics (raw HTML tokens vs. clean markdown tokens):
Generate a static markdown site (one .md file per discovered page):
The markdown engine uses a three-stage pipeline (Sanitize, Extract, Convert) to strip boilerplate, navigation, ads, and scripts, producing clean markdown that typically achieves 70%+ token reduction. See docs/markdown-engine.md for details.
Reverse Proxy Server
Serve markdown to AI agents automatically via Accept: text/markdown content negotiation:
When an AI agent sends a request with Accept: text/markdown, the proxy fetches the upstream HTML, converts it through the markdown engine, and returns clean markdown. Regular browser requests receive the original HTML unchanged.
V3 Scoring
Use the V3 scoring model with the Agent Readiness pillar:
V3 adds a 20-point Agent Readiness pillar and rebalances the existing pillars. See docs/scoring-v3.md for the full methodology.
Start MCP server
Launches a FastMCP stdio server exposing the linter as a tool for AI agents.
MCP Integration
To use Context CLI as a tool in Claude Desktop, add this to your Claude Desktop config (claude_desktop_config.json):
Once configured, Claude can call the audit_url tool directly to check any URL's LLM readiness.
New MCP Tools (v3.0)
In addition to the existing tools (audit, generate, compare, history, recommend), v3.0 adds:
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
4
Stargazers on the source repository.
Last commit
5mo ago
Most recent push to the default branch.
Directory activity
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CategorySearch & Data Extraction
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Scanned 27d ago via OSV.dev · context-linter (PyPI)
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