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Agent-first local SEO scoring, search-intent and opportunity engine with CLI and optional MCP server.
mcp-name: io.github.davidmosiah/agent-seo-engine
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🧱 Part of the Delx agent stack — 15 open-source MCP servers across body, reach and coordination.
Agent-first SEO scoring, search-intent detection and opportunity prioritization. It packages the useful parts of a production content pipeline into a clean local CLI plus an optional MCP server for Codex, Claude, Cursor, Hermes, OpenClaw and other agent runtimes.
Use it when an agent needs deterministic SEO checks before rewriting, refreshing or publishing content.
manifest, connection_status and privacy_audit surfaces before content toolspipx install agent-seo-engine
With MCP support:
pipx install "agent-seo-engine[mcp]"
Published on PyPI: agent-seo-engine. Release automation uses PyPI Trusted Publishing, so GitHub Actions can publish future versions without long-lived PyPI tokens. See docs/pypi-publishing.md.
agent-seo-engine manifest --client codex
agent-seo-engine doctor
agent-seo-engine privacy-audit
agent-seo-engine intent "best ai agent framework"
agent-seo-engine score --file examples/article.md --primary-keyword "ai agent testing"
agent-seo-engine opportunity --impressions 4200 --clicks 80 --position 12.4 --commercial-intent 0.8
agent-seo-engine image-alt --file page.html
All commands return structured JSON by default. Use --format markdown for human review.
agent-seo-mcp
Hermes-style config:
mcp_servers:
agent_seo:
command: agent-seo-mcp
args: []
sampling:
enabled: false
Recommended first calls:
agent_seo_connection_statusagent_seo_privacy_auditagent_seo_score_content| Tool | Purpose |
|---|---|
agent_seo_manifest | Install/runtime guidance for agent clients |
agent_seo_connection_status | Local/offline readiness and optional integration status |
agent_seo_privacy_audit | Draft, analytics and credential boundaries |
agent_seo_detect_intent | Search intent classification |
agent_seo_score_content | Markdown quality checks with exact recommendations |
agent_seo_prioritize_opportunity | GSC-style opportunity scoring |
agent_seo_check_image_alt | Image alt-attribute coverage audit for HTML |
Use agent-seo-engine. First call agent_seo_connection_status and agent_seo_privacy_audit.
Score the draft, then propose only edits tied to failed checks or high-impact opportunities.
Agents should not guess whether a draft is ready. They should call the scoring tool, read exact failed checks, then propose focused edits. The engine is intentionally deterministic and local so repeated agent runs can compare output over time.
python3 -m venv .venv
. .venv/bin/activate
pip install -e ".[dev]"
pytest
python -m compileall -q src
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