KernelMind MCP
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- AlicenseNot gradedqualityCmaintenanceEnables AI coding agents to query repository structure, symbols, dependencies, Git history, architecture, memory, impact, and guarded edits through a stable MCP interface, returning ranked task-specific evidence within a context budget.50 PyPI8Apache 2.0
- FlicenseNot gradedqualityBmaintenanceEnables LLMs to query a local commitment ledger and knowledge base extracted from email, calendar, and meeting transcripts, allowing natural language search for facts, people, and open commitments, and suggesting closures with evidence.-
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- AlicenseAqualityAmaintenanceEnables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.21MIT
TDQS
Scored across 16 tools
Every tool targets a distinct resource type and action. Search tools are differentiated by target (code, commits, patches, LKML, docs, LWN), and detail tools map one-to-one to those search targets. Even similar tools like search_kernel_code and lookup_symbol are clearly separated by broad search vs. exact symbol lookup.
All tool names follow a consistent lowercase snake_case verb_noun pattern (search_*, get_*, lookup_symbol, blame_line_range, analyze_*). No mixed conventions or ambiguous verbs; the naming scheme is uniform and predictable.
At 16 tools, the set is slightly above the typical 3-15 ideal range, but the breadth of the domain (code, commits, patches, LKML, docs, LWN, risk analysis) justifies each tool's existence. No redundant or trivial tools are present.
The tool surface provides full lifecycle coverage for kernel intelligence: search and retrieve operations for each data type (code, commits, patches, LKML, docs, LWN), plus specialized analysis (patch series, subsystem risk, file history, blame). No obvious gaps prevent an agent from answering kernel-related questions or performing deep investigations.