colregs-mcp
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- FlicenseNot gradedqualityAmaintenanceExposes 20+ tools that let any MCP-compatible client run hybrid retrieval over MPEP/USC/CFR regulations, search 100M+ granted and published patents via Google BigQuery for prior art, and perform automated 35 USC 112 compliance checks on draft claims and applications. It also generates publication-style diagrams and flowcharts from natural-language descriptions.-

Magist MCP Serverofficial
AlicenseNot gradedqualityBmaintenanceConnects AI hosts to a deterministic, cited regulatory-compliance reference so agents can look up laws, search regulations, compute which requirements a product triggers, and find related Controls, vendors, enforcement actions, and counsel with primary-source links. It also exposes the accepted feature/market/audience catalog and a token verifier for grounding what was served.40 npmMIT- FlicenseNot gradedqualityAmaintenanceExposes 20+ tools that let MCP-compatible clients answer patent-rule questions via hybrid RAG search over MPEP, USC, and CFR, hunt for prior art across 100M+ patents through Google BigQuery, run automated claim and application compliance checks, and generate patent-style diagrams with Graphviz.-
- FlicenseNot gradedqualityAmaintenanceEnables MCP-compatible clients to search patent rules and prior art across MPEP/USC/CFR and 100M+ patents, run automated 35 USC 112 compliance checks, and generate patent-style diagrams.-
- FlicenseNot gradedqualityAmaintenanceEnables patent-rule lookup with citations over MPEP/USC/CFR via hybrid vector, lexical, and reranked search, plus prior-art searching across 100M+ patents through Google BigQuery. Also runs automated 35 USC 112 compliance checks on draft claims and applications and generates patent-style diagrams and flowcharts.-
- AlicenseNot gradedqualityCmaintenanceA modular MCP server providing structured, methodology-driven prompt endpoints for rapid legal/compliance landscape mapping, gap discovery, and risk prioritization across privacy, IP, AI governance, security, consumer, and disclosure domains, with argument-based adaptability to any product context.5 npm6MIT
TDQS
Scored across 3 tools
The three tools have clearly distinct roles: evaluate runs the COLREGS assessment engine, light returns a single light definition by id, and rule_text returns verbatim rule text by cite. Although evaluate's output references light ids and paragraph cites, there is no functional overlap with the two lookup tools.
The names are readable and all lowercase/snake_case, but they do not follow a single convention: evaluate is a verb while light and rule_text are noun-style lookup names. The mixed convention is minor given only three tools, but it is not a predictable verb_noun pattern.
Three tools is slightly thin but reasonable for a narrowly scoped COLREGS lights evaluator. Each tool serves a distinct purpose: assessment, light-definition lookup, and rule-text retrieval.
The core workflow is covered: evaluate a fact record, look up any light returned, and retrieve cited rule paragraphs. Minor gaps remain, such as no enumeration of all lights or rules and explicitly no compliance-over-time reading, but these are not fatal for the stated preview scope.