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Index codebases and return query-relevant context packages for AI agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-intelligence-context" yet — see the docs or source repo.
Using the codebase index, return the symbols, imports, and dependencies related to paymentService.processRefund as a compact context package for an AI model.
A compact code context focused on the target function instead of a full repository scan.
Retrieve the key symbols and upstream/downstream dependencies of the auth module in the repository, and output a context package suitable for further AI analysis.
A summary of relevant symbols, import relationships, and dependency chains for the auth module.
I want to ask AI, 'Why does this caching layer affect order lookup?' First extract the most relevant symbols, imports, and dependencies from the index and build a high-relevance, low-token context package.
A compact context package for code Q&A that helps the model understand the issue faster.
Before asking AI to analyze or modify code, developers can use it to index the repository and fetch relevant symbols, imports, and dependencies for the task. This avoids scanning the entire repo and reduces token usage.
When you need to understand the upstream and downstream relationships of a function, module, or component, it can return a query-filtered context package. This is useful for tracing complex code paths or preparing inputs for deeper AI reasoning.
In large codebases, sending full files to a model is expensive. This tool uses indexing and retrieval to provide leaner, query-relevant context and improve AI efficiency.
It provides indexing and retrieval for codebases, returning query-relevant context packages to AI agents, including symbols, imports, and dependencies. Its main benefit is avoiding full repository scans and reducing token costs.
It focuses on returning compact context that is most relevant to the current query instead of exposing the whole repository to the model. This is usually more efficient and better suited for task-based AI agents.
The provided material does not include installation steps, runtime requirements, or API key details. Please see the source repository for specifics.
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