ChipsAI MCP Server

ChipsAI MCP Server

MCP (Model Context Protocol) server for ChipsBot — manage chatbots, conversations, documents, bot-to-bot routing, RAG configuration, and AI models from Claude Code, Claude Desktop, or any MCP client.

Requirements

• Python 3.11+
• uv (recommended) or pip
• A ChipsBot account (sign up)

Quick Start

No installation needed with uv:

uv run --script server.py

Or install manually:

pip install "mcp[cli]" httpx

python server.py

Configuration

The server uses environment variables for authentication. API key is the recommended method — generate one from your ChipsBot dashboard.

Legacy: username/password authentication

If you don't have an API key, you can use username/password instead:

Claude Code

Add to your project's .mcp.json:

{

"mcpServers": { "chipsai": { "command": "uvx", "args": ["chipsai-mcp"], "env": { "CHIPSAI_API_KEY": "chipsai_your_api_key_here" } } } }

Claude Desktop

Add to claude_desktop_config.json:

{

"mcpServers": { "chipsai": { "command": "uvx", "args": ["chipsai-mcp"], "env": { "CHIPSAI_API_KEY": "chipsai_your_api_key_here" } } } }

Available Tools

Chatbot Management

Documents (RAG)

Conversations

Widget History

Chat

Bot-to-Bot Connections

RAG Configuration

User & Models

RAG Pipeline

ChipsBot supports a full Retrieval-Augmented Generation pipeline configurable per-bot:

• Semantic routing (L1): pgvector + Jina Embeddings v3 — routes queries to the best specialist based on cosine similarity (HNSW index)
• HyDE: for sparse/short queries, generates a hypothetical answer with Haiku and re-embeds it for better retrieval
• Chunk injection (L2): at response time, injects only the top-K relevant KB chunks instead of the full prompt — reduces token usage, improves quality
• Reranking: optional Jina cross-encoder reranker (jina-reranker-v2-base-multilingual) applied after cosine retrieval
• Chunking strategies: char (fixed size), paragraph (semantic \n\n split), sentence (.!? split)
• Document upload: PDF/DOC/DOCX parsed via LlamaParse, extracted text stored as KB

Use get_rag_config / update_rag_config to tune all parameters per-bot.

Bot-to-Bot Routing

An orchestrator bot can route questions to specialist bots based on role/description. The orchestrator detects [ROUTE:uuid] tags in its own response and delegates to the matching specialist, passing recent chat history as context.

Use connect_bot to link specialists to an orchestrator, list_bot_connections to inspect the routing table, and update_bot_connection to adjust roles or toggle connections on/off.

Credit System

ChipsAI uses a credit-based pricing model:

Credit packages: 150 credits for €5 | 700 for €20 | 2000 for €50. Credits never expire. Bring your own API key to use any model for free (no credits consumed).

Usage Examples

Once configured, use natural language in Claude:

• *"List my chatbots"*
• *"Create a chatbot called Support Bot"*
• *"Upload the product catalog PDF to my chatbot"*
• *"Send a test message to my chatbot"*
• *"Show analytics for the last 7 days"*
• *"Change the chatbot model to Claude Sonnet 4.6"*
• *"What's my credit balance?"*
• *"What AI models are available?"*
• *"Connect the billing bot as a specialist of my main orchestrator"*
• *"List all specialist bots connected to my orchestrator"*
• *"Show the RAG config for my chatbot"*
• *"Set the RAG threshold to 0.5 and enable reranking"*
• *"Enable L2 chunk injection with top_k=5"*

Authentication

API Key (recommended): Set CHIPSAI_API_KEY with a key generated from your dashboard. The key is sent as a Bearer token — no token management needed.

JWT (legacy): If using username/password, tokens are obtained via JWT and refreshed transparently.

License

MIT