Skip to main content
Glama

gapup-mcp

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

A4.6/5.0
Behavior5/5

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

Annotations already set readOnlyHint=true and destructiveHint=false; the description goes beyond by specifying SLA (24s p95), cache TTL (24h), source selection behavior, and a quality scoring formula. This adds meaningful operational context without contradicting the annotations.

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

Conciseness4/5

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

The description is dense but efficiently structured with numbered modes, bullet-like source lists, and clear use cases. It is longer than average, but each clause provides operational value (SLA, cache, scoring), so it earns its place without being bloated.

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?

For a complex multi-mode tool with 7 parameters, an async flag, and external sources, the description covers modes, sources, use cases, performance expectations, and quality scoring. An output schema exists, so omitting return-value details is appropriate. The only minor gap is not mentioning the async pattern in the description itself, but the schema covers it.

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

Parameters4/5

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

Input schema has 100% coverage with descriptions for every parameter. The description supplements this by explaining the three modes in detail and clarifying that 'query' can be keywords, a company/inventor name, or a patent number—semantics not fully captured by the schema alone.

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 opens with a specific verb and resource ('Search, analyze and map patent landscapes') and then details three distinct modes (search, landscape, lookup), making the tool's purpose unmistakable. It clearly distinguishes from siblings like patent_ownership_audit by focusing on landscape mapping across jurisdictions.

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?

Provides explicit use cases (FTO analysis, R&D gap identification, VC due diligence, competitor mapping, inventor network analysis) that signal when to invoke the tool. It doesn't name direct alternatives or exclusions, but the use-case list is strong contextual guidance.

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

All gapup-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

Resources