One MCP server. Five AI pipelines. Thirty-one tools. Zero manual setup.
Composable, agentic AI pipelines for Anthropic's Model Context Protocol. Fact-check claims with DSPy, search documents with hybrid RAG, extract intelligence with DeepPipe, and run clinical voice intake — all from a single MCP server. Designed for Claude Desktop, VS Code, Cursor, and any MCP-compatible client.
| Tool | What it does |
|---|---|
deeppipe_search |
Full-text + vector hybrid search across all documents |
deeppipe_ingest |
Ingest raw text/JSON documents for indexing |
deeppipe_ingest_file |
Ingest documents directly from file paths |
deeppipe_chat_context |
RAG-style Q&A with source citations |
deeppipe_extractive_answer |
Extract a precise answer from documents |
deeppipe_list_documents |
List all indexed documents with metadata |
deeppipe_get_document |
Retrieve a specific document by ID |
deeppipe_get_text |
Get the full text content of a document |
deeppipe_remove_document |
Remove a document from the index |
deeppipe_stats |
View index statistics and document count |
| Tool | What it does |
|---|---|
piste_fact_check |
Run a claim through the full 4-stage DSPy pipeline (retrieval → verification → aggregation → verdict) |
piste_list_verdicts |
Browse all past fact-check verdicts |
piste_replay |
Replay the audit trail of any fact-check run |
piste_get_audit |
Get the detailed reasoning chain for a verdict |
piste_get_verdict |
Retrieve a specific verdict by claim ID |
piste_submit_feedback |
Submit human feedback to improve future checks |
| Tool | What it does |
|---|---|
precis_query |
Full RAG query — hybrid search + LLM answer generation |
precis_list_documents |
List all documents in the RAG corpus |
precis_debug_stem |
Inspect how the stemmer processes a query |
precis_debug_search |
See raw hybrid search results (before LLM) |
precis_upload_document |
Upload a document to the RAG corpus |
precis_upload_batch |
Batch-upload multiple documents at once |
precis_extract_work_order |
Extract structured work orders from documents |
precis_list_work_orders |
Browse all extracted work orders |
| Tool | What it does |
|---|---|
clinical_start_session |
Start a new voice intake session (returns session ID) |
clinical_process_audio |
Send audio → STT transcription → SOAP notes |
clinical_generate_podcast |
Generate a clinical podcast from session notes |
clinical_list_sessions |
List all voice intake sessions |
clinical_get_session |
Get full details of a specific session |
| Tool | What it does |
|---|---|
mcp_health |
Server health, tool count, provider status |
mcp_list_providers |
List all 9 supported LLM providers |
These tools are designed to be chained. Here's a real workflow:
1. deeppipe_ingest → Load a research paper into the index
2. precis_upload_document → Add it to the RAG corpus
3. deeppipe_search → Find relevant passages
4. piste_fact_check → Verify claims found in those passages
5. precis_query → Generate a RAG answer from verified context
6. clinical_start_session → Start voice notes on the findings
7. clinical_generate_podcast → Turn it all into a shareable podcast
In Claude Desktop, this is a conversation:
"Search my documents for claims about climate policy, fact-check each one, then generate a clinical podcast summarizing the verified findings."
Claude orchestrates the tool calls — your server provides the pipelines.
graph LR
subgraph Top[" "]
direction LR
CL[Claude Desktop]
VS[VS Code]
CU[Cursor]
end
subgraph Core["MCP Server Core"]
direction LR
ROUTER[Request Router<br/>6 handlers]
RATE[Rate Limiter]
GUARD[Dep Guard]
end
subgraph DP["DeepPipe<br/>10 tools · TS"]
D1[search] --> D2[ingest] --> D3[chat]
end
subgraph PS["Piste<br/>6 tools · Python"]
P1[fact_check] --> PY1[DSPy Pipeline]
end
subgraph PR["Precis<br/>8 tools · Python"]
R1[query] --> PY2[RAG Engine]
end
subgraph CLN["Clinical<br/>5 tools · TS"]
C1[session] --> C2[audio] --> C3[podcast]
end
subgraph Bottom[" "]
direction LR
LLM[9 LLM Providers<br/>DeepSeek · OpenAI · Anthropic · Groq · Ollama · Google · Azure · OpenRouter · Custom]
EXT[Tavily · Serper · ElevenLabs]
DEP[uv · 10 pip pkgs · 6 npm pkgs]
end
Top --> Core
Core --> DP & PS & PR & CLN
DP & PS & PR & CLN --> Bottom
style Top fill:#f8f9fa,color:#333
style Core fill:#e8f4fd,color:#333
style DP fill:#f0f8ff,color:#1a5276,stroke:#aed6f1
style PS fill:#fef9e7,color:#7d6608,stroke:#f9e79f
style PR fill:#eafaf1,color:#1e8449,stroke:#a9dfbf
style CLN fill:#fdedec,color:#922b21,stroke:#f5b7b1
style Bottom fill:#f8f9fa,color:#333
# 1. Install (npm + Python deps handled automatically)
npx mcp-agentic-pipelines setup
# 2. Start the server
npx mcp-agentic-pipelines
# 3. Or run the test suite
npx mcp-agentic-pipelines testAdd to your MCP client config:
{
"mcpServers": {
"mcp-agentic-pipelines": {
"command": "npx",
"args": ["mcp-agentic-pipelines"]
}
}
}Copy .env.example to .env and set your keys:
| Key | Purpose |
|---|---|
DEEPSEEK_API_KEY |
Default LLM (DeepSeek) |
OPENAI_API_KEY |
Fallback LLM |
GROQ_API_KEY |
Clinical voice (STT) |
ELEVENLABS_API_KEY |
Clinical voice (TTS) |
TAVILY_API_KEY |
Piste web search |
SERPER_API_KEY |
Piste web search |
Server starts with any subset — missing keys skip their tools gracefully.
- Node.js ≥ 18
- Python 3.11+ (automatically managed via
uv— no manual Python install needed on Linux/macOS) - API keys for the tools you want to use (DeepSeek, Groq, ElevenLabs, Tavily, etc.)
git clone https://github.com/jinan-kordab/mcp-agentic-pipelines.git
cd mcp-agentic-pipelines
npm install
node setup.mjs # one-time — installs all Python deps
node test.mjs # runs all 31 tool testspackages/
├── core/ Shared types, config, logging, Python bridge
├── server/ MCP entry point, request routing
├── deeppipe/ 10 document intelligence tools
├── piste/ 6 fact-checking tools (Python bridge → DSPy)
├── precis/ 8 RAG tools (Python bridge → hybrid search)
├── clinical/ 5 voice intake tools (native TypeScript)
vendors/
├── piste/ DSPy pipeline modules + stdin/stdout bridge
├── precis/ RAG backend modules + stdin/stdout bridge
├── clinical-intake/ Voice pipeline (npm workspace)
setup.mjs One-time setup (uv, npm, pip)
test.mjs Complete test suite (31 tools)
MIT © Jinan Kordab
packages/
├── core/ Shared types, config, logging, Python bridge
├── server/ MCP entry point, request routing
├── deeppipe/ 10 document intelligence tools
├── piste/ 6 fact-checking tools (Python bridge → DSPy)
├── precis/ 8 RAG tools (Python bridge → hybrid search)
├── clinical/ 5 voice intake tools (native TypeScript)
vendors/
├── piste/ DSPy pipeline modules + stdin/stdout bridge
├── precis/ RAG backend modules + stdin/stdout bridge
├── clinical-intake/ Voice pipeline (npm workspace)
setup.mjs One-time setup (uv, npm, pip)
test.mjs Complete test suite (31 tools)
MIT © Jinan Kordab