DataStruk provides a Model Context Protocol (MCP) platform that makes any code repository instantly understandable to any LLM, including Claude, Groq, OpenAI, or self‑hosted models. By running a lightweight MCP server, it reads files, maps project structure, analyzes git history, and detects code patterns, exposing this intelligence through REST APIs, webhooks, and real‑time monitoring for developers and teams.
Funding
Funding not disclosed
Founders
Product
Problem
Developers often need to provide AI assistants with detailed context about their codebases, but manually copying files, explaining project structure, and tracking changes across different LLMs is time‑consuming and error‑prone. This limits the usefulness of AI‑driven code assistance, especially in teams that use multiple AI providers or need insights from version‑control history.
Solution
DataStruk offers a Model Context Protocol (MCP)‑first platform that makes any code repository instantly understandable to any LLM, including Claude, Groq, OpenAI, or local models. By running a lightweight MCP server, the tool reads files, maps project structure, analyzes git history, and detects code patterns, then streams this intelligence to the connected LLM. The server provides REST APIs, webhooks, and real‑time monitoring, allowing developers, product managers, and teams to query file content, view architecture health, and receive AI‑generated recommendations without manual context preparation. Because the protocol is open, the same intelligence can be reused across different AI backends, ensuring consistent analysis regardless of the chosen model.
Target Audience
Primary users are individual developers, development teams, and tech‑focused product managers who need AI‑enhanced code insight across multiple LLM providers.
Features
- MCP server that exposes file reading, folder mapping, git‑history analysis, and pattern detection as standardized tools
- Universal integration with any LLM via the Model Context Protocol, supporting Claude, Groq, OpenAI, and self‑hosted models
- File Intelligence Suite: smart file content retrieval, similarity detection, and relationship analysis
- Git Intelligence Suite: timeline reconstruction, change impact analysis, and branch comparison
- Code Pattern Suite: naming consistency checks, duplication detection, and style validation
- AI Agent Intelligence: detection of AI‑generated code, context‑drift tracking, and actionable recommendations
- Project Intelligence Suite: architecture health assessment, dependency mapping, and auto‑documentation generation
- REST API, webhooks, and real‑time monitoring for automated workflows and CI/CD integration