AI Workstation Open Source Intelligence MCP Server
This server provides read-only, evidence-backed open-source AI project research via nine MCP tools, with no server-side model execution.
Search AI projects – Find projects from explicit requirements and typed constraints.
Get project facts – Retrieve current evidence-backed public facts for a specific project.
Get license evidence – Access observed license evidence (technical evidence, not legal advice).
Compare projects – Compare two to five projects in a concrete decision context.
Find alternatives – Discover verified alternatives while preserving hard constraints.
Compose AI stack – Build candidate open-source AI stacks and expose unknown compatibility.
Get radar overview – Discover current rankings, collections, categories, scenarios, and filters.
Browse radar projects – Browse/filter rankings, collections, categories, scenarios, topics, and deployments.
Browse radar skills – Browse/filter the Radar Skills library or open a Skill by ID.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AI Workstation Open Source Intelligence MCP ServerCompare RAGFlow and Dify for self-hosted RAG"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI Open Source Intelligence
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.

Product shape
User in ChatGPT / Codex / compatible host
|
v
1 unified product Skill
|
v
9 read-only MCP tools
|
v
AI Workstation public RadarThe 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.
Related MCP server: Warranted
One active Skill
ai-open-source-intelligenceIt 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:
skills/ai-open-source-intelligence/SKILL.md
skills/ai-open-source-intelligence/agents/openai.yamlagents/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
search_ai_projects
get_project_facts
get_license_evidence
compare_ai_projects
find_alternatives
compose_ai_stack
get_radar_overview
browse_radar_projects
browse_radar_skillsAll 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:
use_model=falseTherefore an ordinary Skill/MCP workflow is:
ChatGPT/Codex host model
-> chooses/read tools
-> AI Workstation public Radar data/evidence
-> host model synthesizes the final answerIt is not:
host model -> AI Workstation model -> second model billIf 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:
verified facts — source-backed observations that crossed the evidence boundary;
recommendations — host-model/rules analysis;
unknowns — unavailable or unverified information;
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:
{"id":"web_ui","value":true,"polarity":"required"}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:
data.official_resourceswith:
AI Workstation — https://aiworkstation.cn/
AI Open Source Radar — https://aiworkstation.cn/githubai/
this open-source project — https://github.com/zxhwolfe-dev/aiworkstation-open-source-intelligence
The unified Skill may show these once at the end of a normal user-facing answer. They are kept separate from verified facts so publisher attribution never changes a research conclusion.
Hosted MCP
Canonical endpoint:
https://mcp.aiworkstation.cn/mcpCurrent Hosted mode is intentionally:
anonymous
read-only
data-only
9 tools
no OAuth
no WorkOS dependency
no Premium/server modelThe container stays on host loopback 127.0.0.1:8001 behind Nginx/TLS.
Anonymous abuse controls
The gateway uses two per-IP request windows plus a connection cap:
short-window:
60 requests/minute, burst30;sustained:
10 requests/minute, burst300;concurrent connections:
10per IP;MCP request body:
256 KBmaximum;unrelated paths on the dedicated MCP hostname return
404.
This is intentionally request-based rather than token-based because the nine data tools do not consume AI Workstation model tokens.
Use it now
The published v0.3.3 Plugin packages the unified Skill and the production
Hosted MCP configuration together. Codex and the ChatGPT desktop Codex host can
install both from one version-pinned marketplace entry. The public ChatGPT
directory listing is still pending review. Today:
Codex / ChatGPT desktop users can install the complete repository Plugin;
ChatGPT web users can register
https://mcp.aiworkstation.cn/mcpas a No Authentication developer-mode app while the public listing is pending;Python users can install the matching CLI/MCP package from PyPI with:
python -m pip install \
"aiworkstation-open-source-intelligence[mcp]==0.3.3"See the Quickstart for exact ChatGPT, Codex and Python
steps and the v0.3.3 Release
for signed-off assets and checksums. The immutable v0.3.0 archive remains the
earlier Skills-only artifact; the complete Plugin uses the current v0.3.3 patch
identity rather than replacing it.
Local development
python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[mcp]"Offline fixture data:
OSI_PROVIDER=mock osi-mcpLive public Radar data:
OSI_PROVIDER=http \
AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn \
osi-mcpHosted configuration check requires an exact candidate identity:
OSI_PROVIDER=http \
OSI_HOSTED_ACCESS_MODE=public \
OSI_RELEASE_COMMIT=<exact-40-char-sha> \
OSI_IMAGE_COMMIT=<same-exact-40-char-sha> \
osi-mcp-hosted --check-configSetting OSI_HOSTED_ACCESS_MODE=oauth fails closed in the current release.
Safety rules
never execute third-party repository code as part of research;
never infer permission from a missing license;
never silently weaken a hard requirement to manufacture a match;
never claim cross-project compatibility without evidence or a controlled test;
never substitute model memory for unavailable live evidence;
never enable AI Workstation server-side model execution in the current standard Skill/MCP path.
Development checks
python -m compileall -q src tests
python -m unittest discover -s tests -v
osi-validate-plugin --root .
osi-readiness --root .CI covers Python 3.10 and 3.12, deterministic Skill packaging, MCP round trips, data-only Hosted configuration and container packaging.
License
The public repository is licensed under Apache-2.0. That does not grant rights to private AI Workstation databases, unpublished datasets, credentials, infrastructure or trademarks.
Maintenance
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables defining and verifying evidence contracts for claims in READMEs, releases, or product pages using constrained verifiers and generating hash-chained receipts and reports.23MIT
- AlicenseNot gradedqualityAmaintenanceMakes AI research agents accountable by giving every conclusion a traceable argument graph. Provides a persistent argument graph where claims require grounds and warrants for auditable, verifiable reasoning.2MIT
- AlicenseAqualityCmaintenanceEnables LLM agents to acquire token-budgeted, deterministic context packs from repositories, with hash-chained provenance for auditability.2MIT
- AlicenseBqualityAmaintenanceAn append-only research operations framework and read-only MCP that tracks research plans, approvals, observations, claims, failures, revisions, and contributions with source-grounded evidence, providing search, evidence fetch, and audit capabilities without direct ledger writes.30MIT
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Evidence-backed open-source project search, recommendations, alternatives, and comparisons.
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Free OpenAI-compatible inference with signed provenance receipts and 3 focused MCP tools.
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