"Target" matching MCP servers:

An MCP server for searching Target products and generating Shopping Cart Share links from a list of TCINs.

Enables AI assistants to interact with the Open Targets Platform API to search and retrieve comprehensive data about target-disease associations, drugs, genes, and drug discovery information through GraphQL queries.

Search and read recipes from the English Wikibooks Cookbook, with tools to rescale ingredient lists to a target number of servings.

Enables AI assistants to query and retrieve a structured professional profile, including expertise areas, CV, projects, active research, and target AI queries, through dedicated MCP tools and resources.

Provides hackathon intelligence for builders by analyzing projects to rank best hackathons, target tracks, and differentiate using fit scoring and public gallery intel.

Enables users to search and retrieve fitness exercise information from the Api Ninjas database. It supports filtering by exercise name, target muscle, type, and difficulty level.

An MCP server that exposes GABI functionality, allowing users to run queries against a target endpoint with proper authentication.

Enables coding agents to retrieve maintainer-written release notes and compare installed versus target dependency versions, search packages, and report on projects pushed to a dashboard.

Enables passive OSINT reconnaissance by providing search dorking and open-source intelligence collection tools through MCP, without requiring active interaction with target systems.

Enables users to search and query Korean business announcements from 기업마당 (Bizinfo) using natural language. Supports both CLI and Node.js client interfaces for retrieving business opportunities by region, target audience, and keywords.

Enables AI agents to search through Memex conversation history and local project files to retrieve specific commands, code snippets, and technology overviews. It utilizes smart context management and faceted filtering to provide relevant search results without causing context overload.

Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.