Coador MCP server

Coador

Give AI agents a map of your Android project before they start reading the code.

Coador scans an Android repository locally and deterministically - without an LLM - and builds a structured, evidence-backed knowledge base covering the project architecture, Gradle configuration, test infrastructure, UI selectors, CI/CD, and other high-value context.

Agents query that knowledge first, then inspect only the files required for the task.

Faster context. Less repeated discovery. Lower token usage.

Coador turns an Android repository into a local knowledge base that AI agents can query instead of repeatedly re-reading and rediscovering the entire project.

The key idea behind Coador is simple: the scanning itself does not use AI or an LLM.

Coador deterministically analyzes an Android project, extracts the information most useful for development and test automation, and stores it in a structured form: architecture signals, Gradle configuration, modules and dependencies, test infrastructure, UI selectors, deep links, CI/CD, test runners, fixtures, dependency-injection overrides, and other project-level knowledge.

As a result, an AI agent does not need to rediscover a large repository from scratch for every new task - searching for the right Gradle files, reconstructing the module structure, identifying the test framework, or figuring out how UI tests are organized.

Instead, the agent can query an already prepared knowledge base and retrieve a compact answer focused only on the part of the project it currently needs.

This reduces repeated repository discovery, lowers the amount of source code that has to enter the model context, and reduces unnecessary token consumption.

Why no AI in the scanner?

Coador deliberately separates fact extraction from AI interpretation.

The repository is scanned locally by specialized deterministic detectors. They do not generate architectural descriptions and do not try to guess how the project is designed. Coador records only signals that can be supported by source files, Gradle configuration, or repository structure.

For example, instead of claiming:

This project uses Clean Architecture.

Coador may report:

domain, data, and feature modules were found, together with the observed

dependencies between them.

The agent receives evidence rather than a pre-generated interpretation and can reason about that evidence itself.

Whenever possible, Coador also preserves the provenance of each finding: the source file and exact source lines, a file-level observation, or a value obtained from the configured Gradle model.

How it works

Coador scans an Android repository and builds a structured local profile of the project. The analysis covers nine main areas:

The complete catalogue currently contains 84 detectors and is generated in docs/detectors.md.

The resulting knowledge is stored locally and can be consumed both by developers and by AI tools. Developers get a CLI and human-readable Markdown views. AI agents get an MCP interface for reading the project overview, searching for information, and retrieving individual sections with supporting evidence.

Shortened. The full README is on GitHub.