Agent Seo Engine

io.github.davidmosiah由 GitHub 账号 davidmosiah 发布v0.1.1更新于 Oct 27, 2026

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

已验证STDIO仅桌面开发者工具数据与分析

概览

AI 生成的概览

本地优先的 SEO 引擎,让助手判断搜索意图、为 Markdown 草稿做 SEO 评分,并对搜索机会排序。

可以做什么
  • 判断搜索意图:信息型、导航型、交易型或商业调研型
  • 为 Markdown 文章做 SEO 缺口评分,给出具体未通过项与修改建议
  • 按曝光、排名、点击率差距、转化与商业价值对 GSC 式机会排序
  • 审计 HTML 页面中图片 alt 属性的覆盖情况
  • 在内容工具之前提供安装指引、本地就绪状态与隐私边界信息
适用场景
适合在助手改写、更新或发布内容之前,用确定性的、可重复的 SEO 检查代替猜测。适用于在本地审阅草稿和搜索表现数据的内容与 SEO 工作流。
运行要求
以本地 Python 包形式运行,用 pipx 安装 agent-seo-engine(MCP 服务器需安装 mcp 附加项),通过 agent-seo-mcp 命令以 stdio 启动。无需 API 密钥,默认完全本地运行。
安装前请注意
服务器会读取你指定的本地草稿文件和 HTML 页面,其隐私审计用于说明草稿、分析数据与凭据的边界。机会评分依赖你提供的曝光、点击和排名等数据,结果质量取决于输入。README 中请求为仓库点星并列出联系方式,属推广内容,并非安装所需。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Agent Seo Engine,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

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 codexagent-seo-engine doctoragent-seo-engine privacy-auditagent-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 0.8agent-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/activatepip install -e ".[dev]"pytestpython -m compileall -q src

📧 Contact & Support

来源:README.md,提交 cb5865e

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.1最新Sep 16, 2026

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