"A service for interacting with company data" matching MCP servers:

Enables natural-language queries over data warehouses with catalog-grounded semantics and per-query authorization, returning answers with attached reasoning.

Exposes data query and auto-insights (anomaly detection) as MCP tools. Enables AI agents to query metrics, list columns, and get data insights directly from pandas.

A production-ready generic FastMCP server template with SQLAlchemy async CRUD, enabling rapid bootstrapping of MCP data-management services. It provides registry-driven CRUD, FK resolution, conflict versioning, and multiple transports.

Enables users to access a local ephemeris database of 111 Chinese remote-sensing satellites, including catalog data, TLE history, sensors, and transit windows, via Streamable HTTP for SIRIAgent.

This MCP server provides secure access to databases for AI agents, enforcing authentication, authorization, human approval, logging, and notifications to prevent dangerous actions.

Enables read-only access to company data across PostgreSQL, MongoDB Atlas, and flat files through MCP tools, allowing AI assistants to query and retrieve information via natural language.

The only MCP server providing structured Chinese fashion supply chain intelligence for AI platforms. No equivalent data source exists in the MCP ecosystem. Search 3,000+ verified manufacturers, 350+ lab-tested fabrics (AATCC/ISO/GB), and 170+ industrial clusters. Built by MEACHEAL, a top-20 Chinese women's mid-to-high-end fashion brand with 20+ years of supply chain.

Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.

Usense Data API now supports the MCP protocol, enabling easy enterprise data access via LLMs through the MCP-compatible usensedata-mcp-server-query-china-company.

Connect any AI to PostgreSQL, MySQL, or SQL Server — 24 dialect-aware tools for query, schema, optimization, and more.

An MCP server that enables Claude to execute SQL queries on MySQL databases with automatic connection management and error handling.

A Model Context Protocol server that enables Claude to execute SQL queries on Snowflake databases with automatic connection lifecycle management.

A server that provides enterprise data services including company information, profiles, business details, shareholders, investments, branches, and key personnel through keyword search and specific queries.

Exposes a fictional B2B CRM database (companies, contacts, deals) as callable MCP tools, enabling AI agents to answer natural-language questions about company records, contacts, and pipeline data.

Provides MySQL database query capabilities through MCP protocol, enabling AI assistants to retrieve table metadata, execute SQL queries, and manage database interactions via Resources, Prompts, and Tools.

An MCP server with 66+ tools for data engineering, enabling AI assistants to interact with Kafka, databases, AWS, data pipelines, infrastructure, and more.

Give your AI agent the entire China A-share market: 55 datasets of prices, financials, capital flows, corporate events and futures/options, stored locally and queryable with read-only SQL. Built point-in-time with delisted stocks kept, so backtests don't lie — no account, no API key.

Enables agents to discover, inspect, and query municipal open datasets from data.seattle.gov through Socrata SODA2, with bounded reads, aggregates, and reference guidance.

Model Context Protocol (MCP) server providing access to 155 normalized Japanese public open datasets (10.16M+ records) including GEPS procurement, healthcare, land prices, and DuckDB SQL.

An MCP server that lets an LLM query a DuckDB warehouse of EuroLeague and EuroCup basketball data in natural language, offering tools for player and team stats, shot charts, boxscores, and fantasy draft analysis without contacting upstream APIs.

MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

Read-only MCP server for querying ClickHouse (product telemetry) and Elasticsearch (download analytics), built for Percona VISTA.

MCP server providing AI assistants access to Polish public registries (KRS, CEIDG) and statistical data (GUS BDL) for querying companies, sole proprietorships, and regional statistics.

Enables agents to explore a database safely and read-only through three tools — list_tables, describe_table, and run_query — served over stdio. Queries are guarded by lexical validation, a capped LIMIT, a read-only SQLite connection, and an authorizer with a timeout.

Enables AI clients to search and read over 2,400 New York City public datasets through the Socrata SODA API, with ready-made tools for restaurant health inspections and 311 service requests plus general dataset discovery and querying.

Lets AI agents and developers inspect and query local MySQL, Redis, and MongoDB instances over STDIO or authenticated loopback HTTP, with read-only defaults and allowlisted non-production writes that require explicit opt-in. Production connections always reject writes, and database-enforced read-only accounts are recommended.

An MCP server for internal data platforms that enables search, viewing, tracing, and read-only querying of business assets like dashboards, metrics, and events.

An MCP server for interacting with SQL Server databases that enables schema inspection, record counting, and paginated data retrieval. It provides tools to explore table structures and fetch specific records while enforcing safety limits on data volume.

Enables answering questions about Ukrainian open data from the data.gov.ua CKAN portal by searching datasets, inspecting resource schemas, and running read-only SQL aggregates over a single tabular resource. Every data-backed answer is computed from retrieved data and returned with machine-readable provenance, and unsupported or unanswerable requests are refused rather than guessed.