clickhouse-mcp-agent 0.9.0

AI agent for ClickHouse database analysis via MCP

ClickHouse MCP Agent

AI agent for ClickHouse database analysis via MCP (Model Context Protocol).

This release reflects a simplified architecture: a single MCP server (mcp-clickhouse) driven by a single agent instance. Access restriction is performed via explicit allow-lists you pass per call (databases/tables), rather than managing multiple keys or fan-out across multiple agents.

Features

• Query ClickHouse databases using AI models
• Structured output: analysis, confidence
• Easy connection management (predefined or custom)
• Conversational context with message-history pruning/summarization
• No CLI or external .env required; configure at runtime
• Single MCP server, single agent lifecycle (no multi-key fan-out)
• Access restriction via per-call allow-lists (allowed_tables)
• Streamable results

Supported Providers

• OpenAI
• Anthropic
• Google Gemini
• Groq
• Mistral
• Cohere

Local Development (Docker)

The fastest way to get started — no cloud ClickHouse account needed:

# Start ClickHouse with seeded demo data (orders + products)
docker compose up -d

# Install the package with dev dependencies
pip install -e ".[dev]"

# Run the examples (set your API key first)
OPENAI_API_KEY=sk-... python examples/example_minimal.py
OPENAI_API_KEY=sk-... python examples/example_stream.py

The docker/init.sql file seeds demo.orders (25 orders, May 2026) and demo.products (10 products across Electronics, Sports, Home, Books) automatically on first start.

Quickstart

• Set model/provider and API key using the runtime config
• Instantiate ClickHouseAgent and call run() or run_stream()

import asyncio
from agent.clickhouse_agent import ClickHouseAgent
from agent.config import config

config.set_ai_model("openai:gpt-4o-mini")
config.set_model_api_key("openai", "your_api_key_here")
config.set_clickhouse(host="localhost", port="8123", user="default", password="", secure="false")

async def main():
    agent = ClickHouseAgent()
    result = await agent.run(
        allowed_tables=["orders", "products"],
        query="give me some insights on the recent data",
    )
    print("Analysis:", result.analysis)
    print("Confidence:", result.confidence)

asyncio.run(main())

Switching providers

All providers use the same interface — just swap the model string and key:

# Anthropic
config.set_ai_model("anthropic:claude-haiku-4-5-20251001")
config.set_model_api_key("anthropic", "your_key")

# Google
config.set_ai_model("google-gla:gemini-2.0-flash")
config.set_model_api_key("google", "your_key")

# Groq
config.set_ai_model("groq:llama-3.3-70b-versatile")
config.set_model_api_key("groq", "your_key")

• For multi-turn conversations, pass message_history between calls. If token usage grows, the agent can summarize history (see below).

Message History & Summarization

• History processing is handled in agent/history_processor.py.
• Summarization behavior is controlled via agent.config.summarize_config (model, provider, token limit).
• When token usage exceeds the configured limit, older messages are summarized into a compact form.

Output

Each call to ClickHouseAgent.run() returns a RunResult with:

• messages: Full (possibly pruned/summarized) message history.
• new_messages: Only messages created in the latest turn.
• last_message: The last message in the conversation.
• usage: Token/usage statistics for the run.
• analysis: Natural-language result text from the model.
• confidence: Confidence level (1-10).

Requirements

• Python 3.10+
• AI API key for your provider (OpenAI, Anthropic, Google/Gemini, Groq, Mistral, Cohere)

All dependencies are managed via pyproject.toml.

Roadmap

✅ Done

• MCP integration via pydantic_ai.mcp.MCPServerStdio
• SQL generation/execution via MCP tools
• Schema inspection (databases/tables/columns)
• Config-driven connections (playground/local/custom)
• Access restriction via per-call allow-lists (allowed_tables)
• Runtime provider/model selection and API key management
• Structured outputs (ClickHouseOutput) and RunResult
• Message history pruning/summarization
• Streaming results via run_stream()
• Local development via Docker (docker compose up -d)
• ruff linting, Python 3.13 support, CI hardened

🔨 0.10 — Production hardening

• Custom exception types (MCPConnectionError, AgentExecutionError)
• sql_used extraction from tool call results
• allowed_databases parameter alongside allowed_tables
• async with ClickHouseAgent() context manager
• Persistent MCP server across multiple run() calls
• Integration tests: allow-list, streaming, summarization, error paths

⚙️ 0.11 — Agent API expansion

• Async batch queries (parallel queries in one call)
• ClickHouseAgent.reset() for lifecycle control
• structlog optional dep for structured observability
• pydantic-ai API audit and model ref updates

🔒 0.12 — Stable

• API locked — no breaking changes without a major version
• All known bugs resolved

🔭 Post-1.0 — Future

• Database-agnostic abstraction (Elasticsearch, MongoDB, Postgres)
• FastAPI standalone deployment option

Contributing

Open an issue or pull request for features or fixes.