For AI
Serve sufficient high-density context instead of making every model rediscover meaning from raw implementation.
An assistant asks GitMir for the slice of the product that answers the task it is doing, and states a token budget. GitMir puts what the task named first, then what that reaches, stops at the budget, and says what it had to leave out — a context that cannot describe its own edges invites the reader to assume it is complete. On the published benchmark that came to 92% answer quality on 8.4K tokens of context per question, against 58% on 61.3K for the same model given raw repository context.
Connect the MCP server to your assistant with the key from the workspace.
Have it ask for context with the task in the user's own words and a token budget.
Follow up on anything the answer named — what one part is, what a change reaches, what the product calls something.
Read the benchmark run before you trust the numbers on your own repository.
The context is assembled from one repository's model at one build. It carries no live data, no secrets and nothing outside the code that was read. Ask for a budget too small for the task and the answer is trimmed — it tells you what was dropped rather than pretending to be complete. And the published benchmark is one repository (Supabase, 459,339 lines) and 50 questions graded blind: it is evidence, not a promise about your codebase.
See it on a real product.
Three models are published in full and open without an account. Ask one of them the question this page is about.