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Agent SEO Engine

@davidmosiahRun by its publisher: served from their own domain, namespace checked by the registry.MCP server · External

Agent-first local SEO quality, intent and opportunity engine with CLI and optional MCP server.

SEOv0.1.1MITPython
Freeno charge per call
How to install

Runs on your machine from its package; the steps are below.

Agent SEO Engine
From the README

What it does

  1. Reads
  2. Writes
  3. High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPIagent-seo-enginev0.1.1

Run

uvx agent-seo-engine

MCP client config

{
  "mcpServers": {
    "agent-seo-engine": {
      "command": "uvx",
      "args": [
        "agent-seo-engine"
      ]
    }
  }
}
README3 min read

Agent SEO Engine

Agent SEO Engine

Local-first SEO scoring, search-intent and opportunity engine for AI agents.
Deterministic checks before agents rewrite, refresh or publish content.

PyPI version Python versions License MIT Built for MCP

GitHub stars CI status Part of the Delx agent stack Category

mcp-name: io.github.davidmosiah/agent-seo-engine

⭐ If this agent-first tool helps your workflow, please star the repo. Stars make this tooling easier for other builders to discover and help Delx keep shipping open infrastructure.
🧱 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.

What It Does

  • Classifies search intent: informational, navigational, transactional and commercial investigation
  • Scores markdown articles for agent-readable SEO gaps
  • Prioritizes GSC-style opportunities by impressions, position, CTR gap, conversions and commercial value
  • Exposes manifest, connection_status and privacy_audit surfaces before content tools
  • Runs locally by default with no required API keys

Install

bash
pipx install agent-seo-engine

With MCP support:

bash
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.

CLI

bash
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.

MCP

bash
agent-seo-mcp

Hermes-style config:

yaml
mcp_servers:
  agent_seo:
    command: agent-seo-mcp
    args: []
    sampling:
      enabled: false

Recommended first calls:

  1. agent_seo_connection_status
  2. agent_seo_privacy_audit
  3. agent_seo_score_content

Agent Surfaces

ToolPurpose
agent_seo_manifestInstall/runtime guidance for agent clients
agent_seo_connection_statusLocal/offline readiness and optional integration status
agent_seo_privacy_auditDraft, analytics and credential boundaries
agent_seo_detect_intentSearch intent classification
agent_seo_score_contentMarkdown quality checks with exact recommendations
agent_seo_prioritize_opportunityGSC-style opportunity scoring
agent_seo_check_image_altImage alt-attribute coverage audit for HTML

Copy-Paste Agent Prompt

text
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.

Agent Contract

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.

Development

bash
python3 -m venv .venv
. .venv/bin/activate
pip install -e ".[dev]"
pytest
python -m compileall -q src

📧 Contact & Support

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