"WEMO" matching MCP servers:

Enables AI agents to interact with ClickHouse analytics through a semantic layer, exposing datasets and metrics for natural-language querying.

A demo MCP server with two governed systems (pipeline semantic layer and predictive model) for querying and scoring professional services opportunities with cross-system governance.

Enables an AI agent to turn a messy CRM account list into a ranked list of accounts to work, with every step authorized, scoped, rate limited, and replayable from an audit log.

Exposes a governed semantic layer built on dbt Core and DuckDB, enabling AI agents to query predefined metric definitions for a P&C insurance dataset. Prevents metric hallucination by restricting agents to governed tools and read-only data access.

Enables AI agents to list metrics and dimensions, inspect definitions, and run governed metric queries against Apache Iceberg tables through a semantic layer, without writing SQL or accessing raw rows or columns outside their role.

Gives Claude read-only tools to list leads, inspect normalized business context, preview the deterministic routing decision (including a country inferred from untrusted notes, always flagged for review), inspect a case's ledger state and audit trail, and explain the ordered policy rules. All outputs are read-only, so the assistant can explain why a lead went where it did while every assignment still requires human approval.

Enables AI agents to access banking data APIs and list customers, connections, accounts, and posted transactions, with metering tied to the caller’s own API key.

A Node.js-based MCP server that extracts numbers from text and generates statistical chart data such as bar, pie, and line charts based on frequency. It enables AI agents to perform data extraction and visualization tasks through the Model Context Protocol.

Enables AI agents to interact directly with Cube's API, allowing them to list cubes/views and metadata, run queries, and retrieve generated SQL.

Compresses spreadsheets into compact encodings, enables Q&A on sheet content, and extracts structured order data via LLM calls.

Provides manufacturing demo data across procurement, production, quality, warehouse, and finance categories via MCP Streamable HTTP, enabling LLMs to retrieve mock datasets.

Enables AI agents to inspect a mock data platform locally via SQLite, using read-only tools for schemas, row counts, freshness, queries, and pipeline status without cloud credentials.