"Testing Library" matching MCP servers:

Provides AI-assisted guidance for the Weik.io integration platform, enabling planning, implementation, testing, and deployment of integrations using Apache Camel and specialized Weik.io features.

A financial data server that provides real-time and historical data for A-shares, Hong Kong, and US markets using the AkShare library. It enables users to query market quotes, fundamental information, and technical indicators through the Model Context Protocol.

Exposes ERP data (production orders, inventory, BOM, customers) to any MCP client through a backend-neutral interface, with a mock backend for testing.

Enables agents to synchronize, merge, and reconcile distributed state across nodes using conflict-free last-write-wins element sets with Lamport/vector-clock causality, token-bucket gossip dissemination, and quorum-based commit coordination. It runs entirely on the Python standard library and exposes these primitives to Claude Desktop, Cursor, and Windsurf over the MCP JSON-RPC stdio protocol.

An MCP server and Go library that connects AI assistants to DataHub metadata catalogs, enabling dataset search, schema exploration, lineage tracing, and glossary access.

MCP server for analyzing China's 福彩3D lottery data with deterministic math engines, forward-testing, and safety checks. Enables data import, statistical testing, coverage optimization, risk control, and optional LLM interpretation.

Enables generation of various chart types (bar, line, pie, scatter, radar, heatmap, and more) using ECharts library, returning preview URLs to visualize data through natural language requests.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.

A Model Context Protocol server that enables Claude Desktop to access and analyze Chinese and global financial market data through the AKShare library.

Provides stock market data and trading capabilities using the yfinance library

Enables comprehensive SQLite database management through natural language including database creation, table operations, data CRUD operations, backup/restore, and CSV import/export functionality. Built on FastMCP framework with PySqlit library for reliable database interactions.

Provides access to historical financial data for US, Hong Kong, and A-share markets using the AkShare library. It returns formatted stock market data, including prices and trading volumes, in easy-to-read Markdown tables.

Enables DAG management, monitoring, debugging, and connection testing for Apache Airflow through the MCP protocol.

Analyze survey data (.sav, .csv, .xlsx) through Claude — crosstabs with significance testing, ANOVA, correlation, gap analysis, and publication-ready Excel exports. Upload once, analyze unlimited.

An MCP server built with Smithery that provides a foundation for real estate data harvesting capabilities. Currently includes example functionality for testing and development purposes.

Enables AI assistants to parse, validate, send, and inspect X12 EDI documents via the SignalEDI Core API, with optional QuickBooks integration and a demo mode for keyless local testing.

MCP server for Formula 1 data via the FastF1 library. Ask Claude (or any MCP-compatible client) about race results, lap times, telemetry, standings, pit stops, and qualifying — with historical data back to 1950 via the Ergast API.

A typed stdio/HTTP MCP server that lets agents drive governed portfolio-company data onboarding: profiling sources, inferring entities and joins, proposing canonical mappings, generating and sandbox-testing dbt artifacts, and publishing only human-certified results. It exposes agent and reviewer-only tools, tenant-scoped run/ontology/evidence resources, and guided prompts, keeping every calculation deterministic and every approval with a human.

Enables an AI assistant to turn a plain-language description into a working Apache NiFi 2.x flow, validate and layout the canvas, run test data, golden-file replays and diagnostics, and commit only verified versions to the NiFi Registry. It exposes 44 read-or-write tools for exploring, building, testing, debugging and releasing flows, staying read-only unless writes are explicitly enabled.