Market Fiyatı MCP Server

MCP Server

Expose Turkish grocery price search via MCP

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About

A Model Context Protocol server that turns marketfiyati.org.tr’s grocery search API into MCP-compatible tools, enabling product lookup by keywords, ID/barcode, and similarity queries.

Capabilities

Resources

Access data sources

Tools

Execute functions

Prompts

Pre-built templates

Sampling

AI model interactions

Market Fiyatı MCP Server

The Market Fiyatı MCP server bridges the gap between conversational AI assistants and Turkey’s leading grocery price‑comparison platform, marketfiyati.org.tr. By exposing the site’s search engine as a set of programmable tools, it lets developers inject real‑time market data into AI workflows without the need for custom scraping or API wrappers. This is especially valuable in regions where public e‑commerce APIs are scarce, yet consumers and businesses demand up‑to‑date price comparisons.

At its core, the server offers three intuitive tools: , , and . Each tool accepts a concise set of parameters—keywords, optional geographic coordinates, and search radius—and returns both a human‑readable summary and the raw structured response from the underlying API. This dual format allows an assistant to present concise answers while still providing developers with full access to the data for further processing or analytics.

Developers can integrate Market Fiyatı into a wide range of use cases. For example, a budgeting assistant can answer “What’s the cheapest oat milk near me?” by invoking with location data. A grocery‑delivery bot can verify a product’s identity via barcode using , ensuring the correct item is added to a cart. Retail analytics tools can surface competitors’ pricing by calling , helping merchants adjust their own price points. Because the server follows the Model Context Protocol specification, any MCP‑compatible client—Claude Desktop, VS Code extensions, or custom agents—can discover and use these tools automatically.

What sets this MCP apart is its lightweight, zero‑code integration. Once the server is running, clients can install it with a single command or add a simple JSON entry. The server then registers its tools in the MCP catalog, allowing assistants to prompt users for the necessary parameters and execute searches on demand. This plug‑and‑play approach eliminates the overhead of maintaining separate API clients, enabling rapid prototyping and deployment in production AI systems.