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官方说明7 个来源MCP 服务8 天前
zxhwolfe-dev/aiworkstation-open-source-intelligence

zxhwolfe-dev/aiworkstation-open-source-intelligence

Evidence-backed research, comparison, license verification, and stack planning for open-source AI projects. 1 Skill + 9 read-only MCP tools.
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add aiworkstation-open-source-intelligence -- npx -y aiworkstation-open-source-intelligence
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
3 ★ · zxhwolfe-dev/aiworkstation-open-source-intelligence · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 3 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 zxhwolfe-dev/aiworkstation-open-source-intelligence;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

AI Open Source Intelligence

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

**One Skill. Nine live read-only Radar tools. Evidence-backed open-source AI research without a second server-side model call.**
简体中文 · Product page · AI Open Source Radar · Quickstart
AI Open Source Intelligence is the Skills/MCP product layer for **AI Open Source Radar**.
Production screenshot captured 2026-08-29. Rankings and project data update with Radar snapshots.
Product shape
The user does not choose separate research/comparison/stack Skills. The single Skill routes the task internally.
The **host model** performs natural-language reasoning and synthesis. The AI Workstation server provides data/evidence only on this product path.
One active Skill
It handles:
- browsing rankings, collections, categories, scenarios and the Radar Skills library;
- finding projects from deployment, privacy, integration, budget and license requirements;
- verifying named-project facts and license evidence;
- comparing two to five projects for a concrete use case;
- finding alternatives while preserving hard requirements;
- planning candidate open-source AI stacks and exposing unverified compatibility.
The only product Skill is packaged from:
agents/openai.yaml makes the Skill's dependency on the canonical
ai_open_source_intelligence Hosted MCP explicit while keeping automatic
invocation enabled. This is the Skill-level dependency contract; .mcp.json
is the matching Plugin-level connection contract.
The previous split research/comparison/stack Skill files are removed from the current product and distribution bundle.
Nine standard MCP tools
All nine are read-only. They do not execute or install third-party repository code.
No AI Workstation server-model execution
This is a hard product boundary for the current release.
The Hosted MCP exposes **no Premium model tool**, no checkout tool and no runtime OAuth/Premium switch. Requirement-based selection calls the public Radar selector with:
Therefore an ordinary Skill/MCP workflow is:
It is **not**:
If member-linked server-model capabilities are added later, they must ship as a new reviewed product version rather than being enabled through a hidden environment variable.
Evidence model
Every tool result separates:
1. **verified facts** — source-backed observations that crossed the evidence boundary;
2. **recommendations** — host-model/rules analysis;
3. **unknowns** — unavailable or unverified information;
4. **risks** — license, maintenance, deployment, security and integration limits.
A value in data is not automatically a verified fact. License evidence is deliberately stricter and is technical evidence, not legal advice.
Requirement tools publish the actual typed constraint contract through MCP:
Formal matches are revalidated against project-detail evidence. README excerpts
may directly verify narrow Docker, self-hosting, browser UI and low/no-code
claims; unresolved hard requirements become near-match blockers. A License label
is exposed only as observed_license_label until a direct LICENSE-family source
supports a verified license fact. Response observation time and evidence-source
freshness are reported separately.
Official resources in results
MCP tool results include canonical, non-tracking publisher links under:
来源
awesome · https://github.com/zxhwolfe-dev/aiworkstation-open-source-intelligence
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