jdocmunch-mcp
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- AlicenseNot gradedqualityBmaintenanceIndexes codebases and lets AI agents retrieve precise code snippets (functions, classes, routes) instead of reading entire files, reducing token usage and improving accuracy.242 npm7MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to quickly find relevant code, focus reads on important parts of large files, and pick relevant items from long lists, reducing token usage and search time.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to retrieve only the specific code sections that answer their questions, drastically reducing context usage while running fully locally.1MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents and coding assistants to inspect, search, and extract only the needed PDF pages or sections as low-token Markdown while preserving tables and document structure.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to navigate large Markdown libraries structure-first — walking library → folder → document → section and reading original text with line numbers, with keyword search as fallback — so answers stay explainable and citable without embeddings or a vector database.1MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to navigate long PDFs by address—sections, pages, tables, figures, notes, and terms—so they can read selectively and cite the PDF page behind each answer.12MIT
TDQS
Scored across 64 tools
Descriptions do explicitly carve boundaries (single vs batch, excerpt vs summary vs full content, no-content reads), which rescues an otherwise heavily overlapping set. But there are 8+ section-retrieval variants (get_section, get_sections, get_section_summary/summaries, get_section_excerpt/excerpts, describe_section, get_section_context) and 4+ TOC/inventory variants (get_toc, get_toc_tree, get_document_outline, list_docs, get_doc), so an agent can easily pick the wrong one. Health/diagnostics tools (get_doc_health, doc_health_radar, get_index_overview, verify_index, get_recent_changes) also blur together.
Mostly verb_noun snake_case (index_local, search_sections, get_section, delete_index), but the convention breaks down in places: bare noun names (doc_health_radar, section_neighbors), inconsistent doc_ prefixing (doc_index_repo, doc_list_repos, doc_resolve_repo versus index_local, list_docs, get_doc), and mismatched pairs like get_doc_health vs doc_health_radar and get_doc_pr_risk_profile vs diff_doc_health_radar. Still readable, but the pattern is not predictable.
64 tools for a documentation index/search server is an extreme mismatch — the core surface is index, delete, search, and read, yet the set balloons with telemetry (analyze_perf, get_session_stats, tune_weights), canaries (check_embedding_drift), OpenAPI exploration, glossary lookups, repo groups, PR-risk profiles, and handoff finalization. Many tools are marginal or duplicative, making the surface expensive to discover and easy to misuse.
Coverage of the documentation-index lifecycle is essentially exhaustive: index/refresh from local and GitHub, delete, search (hybrid/lexical/title/counted), full and partial retrieval, outline/navigation graphs, link integrity, staleness/drift, verification, and even cross-suite code bridging. It is hard to name a meaningful doc-index operation that is missing.