A
licenseNot graded
qualityD
maintenanceEnables AI assistants to crawl, index, and retrieve information from technical documentation using semantic search, with optional knowledge graph validation for code hallucination detection.
MIT
LaserData Docs MCP
Officialby laserdata
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TDQS
A4/5.0
Scored across 8 tools
Disambiguation4/5
Most tools have clearly distinct roles: list/get/search/related/map/refresh are all separable, and the doc-key vs keyword distinction is explained. The one soft spot is the trio search_docs, find_examples, and extract_code_snippets, which all surface documentation content including code; descriptions do differentiate them (topic/language search vs. snippet inventory with CI metadata), but an agent could still hesitate between them.
Naming Consistency5/5
Every tool follows a uniform snake_case verb_noun pattern (list_docs, get_doc, search_docs, find_examples, extract_code_snippets, get_related_docs, explain_doc_map, refresh_docs). No camelCase mixing or inconsistent verb styles; the naming is predictable throughout.
Tool Count5/5
Eight tools is well-scoped for a read-only docs server, covering discovery, retrieval, search, examples, snippets, relatedness, guidance, and cache refresh. Each tool has a defensible purpose and none feels redundant or filler.
Completeness5/5
The surface covers the full read-only docs lifecycle: listing the corpus, fetching a page, searching, finding runnable examples, extracting snippets, traversing related pages, orienting via a map, and refreshing the cache. No write operations are expected for a documentation source, so there are no obvious dead ends.
Maintenance
ActivityMaintained
ResponsivenessNo issues