mason
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- FlicenseAqualityDmaintenanceA TypeScript tool that ranks files in your codebase by importance, tracks dependencies, and provides file summaries to help understand code structure through Cursor's Model Context Protocol.14302-
- AlicenseAqualityCmaintenanceExtract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathers and makes concepts, naming conventions, and vocabulary queryable via MCP.1943MIT
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- AlicenseBqualityCmaintenanceIndexes a Python/JS/TS repository into a local SQLite knowledge graph so agents can trace callers, callees, imports, subclasses and covering tests, measure impact radius, rank diff risk, and summarize architecture without grepping files. Exposes these queries as MCP tools alongside an equivalent CLI, with no daemon, embeddings, or network access.10MIT
- FlicenseBqualityBmaintenanceTurns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.30-
- AlicenseNot gradedqualityAmaintenanceTurns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI coding agents with persistent architectural memory of codebases, enabling impact analysis, test generation, and code generation with reduced token usage.MIT
- AlicenseNot gradedqualityBmaintenanceEnables local code intelligence for AI agents and editors by indexing source code, dependencies, Git history, tests, and documentation, and provides change-impact analysis and relevant test selection through MCP.1AGPL 3.0
- AlicenseNot gradedqualityCmaintenanceTransforms codebases into structural knowledge graphs for AI agents and developers, providing precise architectural awareness and dependency mapping.54MIT
TDQS
Scored across 22 tools
Tools are largely distinct: map building, decision lifecycle, advisory review, setup, and orientation each have dedicated operations. Minor ambiguity remains among orientation/read tools such as full_analysis, analyze_project, get_code_samples, get_snapshot, and get_context, though descriptions clarify map/no-map and task-context boundaries.
Names are consistently snake_case and mostly follow a verb_noun pattern. A minor split exists between mason_* prefixed tools and unprefixed tools, and a few noun-like names such as full_analysis and mason_automation slightly break the pattern but remain readable.
22 tools is at the heavy end for a single MCP server and falls into the borderline 16-25 range. The domain is complex, but some setup/lifecycle tools could likely be consolidated, so the set feels heavy rather than tightly scoped.
The surface covers setup, decision capture/review/retirement, map build/verification/drift, impact analysis, advisory review, and Confluence export, which is broad lifecycle coverage. Minor gaps exist around generic listing/deletion/update paths for some artifacts, but agents can generally work around them.