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mcp-knowledge

by io.github.getgapup

patent_landscape

Read-only

Search, analyze and map patent landscapes across major jurisdictions (US, EP, WO, CN, JP, KR). Three modes: (1) search — find patents by keywords, company name or inventor name; (2) landscape — aggregate distributions: top assignees, top inventors, CPC class breakdown, filings by year, citation leaders, white-space innovation opportunities; (3) lookup — retrieve a specific patent by number (e.g. US10000000B2, EP3456789A1, WO2023/123456). Primary source: WIPO PatentScope (WO PCT, keyless). Optional sources: USPTO PatentsView (US, env PATENTSVIEW_API_KEY), EPO OPS (EP/WO, env EPO_OPS_CONSUMER_KEY + EPO_OPS_CONSUMER_SECRET), Lens.org (global, env LENS_API_TOKEN). Use cases: freedom-to-operate (FTO) analysis, R&D gap identification, VC due diligence IP audit, competitor patent portfolio mapping, inventor network analysis. SLA: <=24s p95 (parallel fetches, 8s per source). Cache: 24h TTL (patent data stable). Quality score: 30 pts per retrieved source (max 90), +10 if >=10 patents, +10 bonus for landscape mode with non-empty top_assignees.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosearch: keyword/inventor/assignee search; landscape: aggregate distributions; lookup: fetch by patent number. Default: "search"
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
queryYesKeywords, company/inventor name, or patent number (e.g. "machine learning", "Tesla Inc", "US10000000B2")
date_toNoISO date YYYY-MM-DD — latest filing date
date_fromNoISO date YYYY-MM-DD — earliest filing date
max_resultsNoMax patents to return (5-50). Default: 20
jurisdictionsNoJurisdictions to include. Default: ["US","EP","WO"]

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
queryYes
statusYes
patentsYes
sourcesYes
landscapeNo
quality_scoreYes

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description extensively discloses behavioral traits beyond annotations: SLA (≤24s p95), parallel fetches (8s per source), cache TTL (24h), quality scoring, and async behavior. Annotations already convey readOnly and openWorld hints, and the description adds crucial context about performance and result staleness without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the essential purpose, modes, and sources, then proceeds to use cases, SLA, and technical details. Every sentence serves a clear purpose without redundancy. Despite its length, it remains efficient and well-organized for a complex tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, output schema presence, and complete schema coverage, the description covers all necessary aspects: modes, parameters, sources, SLA, caching, quality scoring. It leaves no significant gaps for an agent to understand invocation and expected behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions (e.g., mode enum, query examples, default values). The description adds minimal further meaning beyond restating these, such as elaborating on mode behavior and offering example use cases. Baseline 3 is appropriate as the schema carries the primary semantic load.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search, analyze and map patent landscapes' and explicitly lists three distinct modes (search, landscape, lookup). It differentiates from siblings like patent_landscape_async and patent_ownership_audit by focusing on synchronous analysis with multiple aggregated outputs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides specific use cases (FTO analysis, R&D gap identification, VC due diligence, etc.) and explains the three modes, giving strong contextual guidance. However, it does not explicitly state when to avoid this tool in favor of alternatives like patent_ownership_audit or the async variants.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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