TinySearch - Give small LLMs fast web access without context bloat
MCP research tool shrinking web context for local agents without hosted dashboards.
Command line artificial intelligence - Context engineering for terminal powerusers
MCP client support and vendor-agnostic piping for terminal AI workflows.
Terminal users building AI workflows
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MCP research tool shrinking web context for local agents without hosted dashboards.
Sandboxed MCP server lets LLMs run Ghidra and Radare2 without blowing up your host.
It builds a local 'context lake' (SQLite + FTS5) that stitches Slack threads, PRs, tickets and docs to precise code locations so queries like "Why did the payment service deploy fail last week?" return linked commits, messages and files. The local-first approach is a sensible privacy + reliability choice and the bi-directional linking of mentions to file:line is the clearest practical win here. That said, the idea sits in a crowded space—quality of connectors and ranking will determine whether this is indispensable or just another repo-chat tool.
Captures architectural decisions once, every agent reads them automatically via MCP.
Versioned AI context inside node_modules beats remote MCP servers for accuracy.
It stitches Slack threads, PRs, tickets and docs into a local "context lake" and can point a mention like handlePayment() straight to the file and related PRs — very practical for debugging and postmortems. The use of SQLite FTS5 for local full-text search plus MCP for a unified context layer is a smart, pragmatic combo; success will hinge on connector reliability and search/ranking quality, not the README.