narrow-mcp

# narrow-mcp <!-- mcp-name: io.github.manik-prakash/narrow-mcp --> An MCP server that narrows large, low-density non-source files (logs, test output, CSV, JSON, HTML) down to the verbatim spans relevant to a stated intent -- verified against the original file, never a summary. ## Why Coding agents burn context reading large low-density files. A 40k-token log might hold a few hundred tokens of signal. This tool: 1. Runs deterministic narrowing first (grep-style search, structural parsing per file type, sampling) -- free, fast, zero LLM cost. 2. If that alone resolves the query with confidence (e.g. an exact CSV "null column" query), returns it directly. No LLM call at all. 3. Otherwise passes only the narrowed candidates (never the raw file) to a cheap, fast selector model that returns line ranges and a one-line reason -- never prose. 4. Re-reads the chosen line ranges from the **original file on disk** and returns that verbatim text. The selector's own words are never trusted or returned -- only its line-number coordinates, which get verified. 5. If the selector fails, times out, or returns something invalid, falls back to the deterministic candidate set rather than failing outright. Non-goals: source code retrieval (use LSP/tree-sitter/ast-grep), prose summarization, local/self-hosted models. ## Status v1, single file per call. Four file types: log/build-output, CSV, JSON (single document or JSONL), HTML. ## Development ``` pip install -e ".[dev]" pytest python eval/run_eval.py # mocked selector, free python eval/run_eval.py --live # real selector call, needs an API key (see Configuration) ``` ## Configuration **You only need to set one API key.** The provider is auto-detected from whichever key is present -- no separate provider/model config required: | If you set... | Provider used | Default model | |----------------------|----------------|----------------------------------------…