Build a ClickHouse Semantic Layer with MCP in LibreChat

To build a reliable semantic layer for ClickHouse NL-to-SQL queries in LibreChat, externalize business terms like “total revenue” or “active users” into human-editable YAML files on your MCP server, mapping them directly to ClickHouse tables and SQL logic without exposing raw schema. Enforce tool invocation by configuring LibreChat’s YAML to mandate MCP endpoints with strict system prompts that require sequential calls like discover_context → generate_sql → validate_sql. This turns unpredictable LLM responses into a governed workflow, perfect for non-technical users querying ClickHouse data.

Contents

• Understanding MCP Servers and Semantic Layers for ClickHouse
• Setting Up a ClickHouse MCP Server
• Building a True Semantic Layer with YAML Mappings
• Configuring LibreChat YAML for MCP Enforcement
• Enforcing Sequential Tool Calls in NL-to-SQL
• Docker Deployment for Reliable MCP Server
• Workarounds and Best Practices for Consistent Queries
• Testing, Troubleshooting, and Scaling
• Sources
• Conclusion

Understanding MCP Servers and Semantic Layers for ClickHouse

MCP servers bridge LLMs like those in LibreChat to external tools, especially for ClickHouse NL-to-SQL workflows. But here’s the rub: stock setups often dump raw ClickHouse tables and columns into prompts, forcing users to know schema details. A semantic layer fixes that. It translates business lingo—“show me daily active users last week”—into precise ClickHouse SQL, hiding complexity.

Why does this matter for your setup? Without it, non-tech folks hit walls, typing awkward prompts with table names. The LY Corp tech blog nails this: build an MCP server that externalizes knowledge into files, enforces a 7-step workflow, and self-trains from queries. Pair it with dbt’s semantic tools via MCP docs, and you’ve got metrics like query_metrics mapping “total revenue” to compiled SQL.

Think of it as a dictionary for your ClickHouse data. Users say “revenue by region,” MCP looks up mappings, spits SQL. No more “DBError: table not found” because the LLM hallucinated.

Setting Up a ClickHouse MCP Server

Start simple: spin up an MCP server tailored for ClickHouse. Official ClickHouse docs guide LibreChat integration, but for semantic power, customize.

Grab a base from GitHub repos or the LibreChat MCP features page. Install via npm or Python—Python’s flexible for ClickHouse drivers.

pip install clickhouse-connect mcp-server-framework

Connect to your ClickHouse instance:

import clickhouse_connect
client = clickhouse_connect.get_client(host='your-clickhouse-host', port=8123)

Expose tools like list_tables, get_schema, but we’ll layer semantics on top. Run it locally first: python mcp_server.py. Test with curl:

curl -X POST http://localhost:8000/mcp/tools/call -d '{"name": "list_tables"}'

Boom—your ClickHouse MCP server is live. But without enforcement, LibreChat might ignore it. Next up: semantics.

Building a True Semantic Layer with YAML Mappings

This is where magic happens. Ditch schema exposure. Create YAML files for business terms, as in the LY Corp example.

Save this as business_terms.yaml:

business_terms:
 DAU:
 term: 'DAU'
 full_name: 'Daily Active Users'
 definition: 'Users with event logs during period, excluding blocked'
 calculation_logic: 'COUNT(DISTINCT user_id) FROM user_event_history WHERE date = {date} AND NOT blocked'
 data_sources: ['user_event_history', 'user_status_history']
 total_revenue:
 term: 'total revenue'
 definition: 'Sum of transaction amounts'
 calculation_logic: 'SUM(amount) FROM transactions WHERE date BETWEEN {start} AND {end}'
 filters: ['region', 'product']

Your MCP server loads this on startup. When a user says “DAU last week,” the semantic tool parses, substitutes params, generates ClickHouse SQL:

SELECT COUNT(DISTINCT user_id) FROM user_event_history 
WHERE date >= '2026-01-09' AND date <= '2026-01-15' AND NOT blocked

dbt MCP docs inspire this: use query_metrics for groupings, get_dimensions for filters. Domain experts edit YAML—no devs needed. Self-train by logging successful queries back to YAML.

What if terms overlap? Add synonyms: synonyms: ['daily actives', 'DAUs']. Handles fuzzy NL inputs perfectly.

Configuring LibreChat YAML for MCP Enforcement

LibreChat’s MCP servers config is key. Edit librechat.yaml:

mcpServers:
 clickhouse-semantic:
 url: http://localhost:8000/sse # Your MCP server
 serverInstructions: |
 You MUST use MCP tools for all database queries. 
 Map business terms to ClickHouse SQL via semantic layer.
 Never generate SQL from internal knowledge.
 default: true # Auto-attach to relevant convos

Set tool_choice: required in your model config. This blocks direct answers—LLM must call MCP.

For ClickHouse-specific: Add custom instructions:

serverInstructions: |
 For NL-to-SQL: Always call semantic_parse → generate_sql → execute_query.
 Business terms only—no table/column names from users.

Restart LibreChat. Now, prompts trigger MCP reliably. Users type “revenue trends?”—bam, tool chain fires.

Enforcing Sequential Tool Calls in NL-to-SQL

LLMs skip tools? Force order. LY Corp mandates 7 steps:

1. discover_context (load YAML semantics)
2. parse_intent (extract terms like DAU)
3. generate_sql (build ClickHouse query)
4. validate_sql (dry-run check)
5. format_sql (optimize)
6. execute_query (run on ClickHouse)
7. deliver_results (format output)

System prompt in MCP/LibreChat:

You must call tools in exact order: discover_context → parse_intent → generate_sql → validate_sql → format_sql → execute_query → deliver_results.
Do not answer directly. Never skip steps.

In LibreChat YAML, expose as sequential chain. If LLM jumps ahead? Tool returns error: “Call discover_context first.”

For ClickHouse quirks—like array joins or materialized views—bake into YAML logic. Results? Consistent NL-to-SQL, even under load.

Docker Deployment for Reliable MCP Server

Scale it. Dockerize your ClickHouse MCP server for prod.

Dockerfile:

FROM python:3.12-slim
COPY . /app
RUN pip install clickhouse-connect mcp-server-framework pyyaml
CMD ["python", "mcp_server.py"]

docker-compose.yml:

services:
 mcp-server:
 build: .
 ports: ["8000:8000"]
 environment:
 CLICKHOUSE_HOST: your-ch-host
 volumes: ['./business_terms.yaml:/app/business_terms.yaml']
 clickhouse:
 image: clickhouse/clickhouse-server

docker-compose up. Links perfectly to LibreChat. Keywords like mcp server docker (50 searches) love this—portable, zero-downtime updates to semantics.

Workarounds and Best Practices for Consistent Queries

Still flaky? Try these:

• Prompt Engineering: Prefix user input: “Use MCP semantic layer for this ClickHouse query: [input]”.
• Fallback Tools: If no MCP hit, route to error: “Query requires semantic mapping—retry with business terms.”
• Hybrid with dbt: dbt MCP for metrics; proxy to ClickHouse.
• Monitoring: Log tool calls in MCP. Spot skips, refine prompts.
• LLM Choice: Claude or GPT-4o follow tools better than lighter models.

Pro tip: Self-training. After success, append to YAML: examples: [{input: "DAU yesterday", sql: "..."}]. LLM learns patterns.

Edge cases? Multi-table joins: Define in YAML as joins: {user_event_history: user_status_history on user_id}.

Testing, Troubleshooting, and Scaling

Test: “What’s DAU on 2026-01-10?” Expect sequential logs, correct ClickHouse SQL.

Troubles?

Scale: Kubernetes for MCP cluster. Monitor with Prometheus. By 2026, this stack handles enterprise NL-to-SQL effortlessly.

Sources

1. Creating a domain-specific NL-to-SQL MCP server
2. dbt Model Context Protocol
3. MCP Servers Object Structure - LibreChat Docs
4. Model Context Protocol (MCP) - LibreChat
5. ClickHouse MCP with LibreChat
6. ClickHouse Blog: LibreChat Agentic Data Stack

Conclusion

A semantic layer via YAML on your ClickHouse MCP server, combined with LibreChat’s enforced tool chains, delivers rock-solid NL-to-SQL—no schema leaks, no skips. Business users query freely; you maintain via files. Start with the YAML mappings and Docker setup today—your workflow transforms from flaky to production-ready.