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mcp-knowledge

by io.github.getgapup

patent_landscape_async

Read-only

Async extended variant of patent_landscape. Supports max_results up to 200 (vs 50 in sync mode) and an optional include_citation_graph flag that enriches each patent with its 2-level citation graph (parent patents that cite this one + child patents cited by this one). Returns immediately (<300ms) with a job_id. Poll the result with patent_landscape_result(job_id) after eta_seconds (~180s). Use for deep R&D white-space analysis, freedom-to-operate (FTO) audits, VC due diligence IP mapping, or large-scale competitor portfolio analysis. Async tool — register a webhook via webhooks_manage(register, url, [job.completed]) to receive callbacks instead of polling. Faster + lighter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosearch / landscape / lookup. Default: "search"
queryYesKeywords, company/inventor name, or patent number (e.g. "machine learning", "Tesla Inc")
date_toNoISO date YYYY-MM-DD — latest filing date
date_fromNoISO date YYYY-MM-DD — earliest filing date
max_resultsNoMax patents to return (5-200). Default: 20
jurisdictionsNoJurisdictions to include. Default: ["US","EP","WO"]
include_citation_graphNoIf true, enriches each patent with a 2-level citation graph (parents + children). Adds significant processing time — use for deep analysis only. Default: false.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesUnique job identifier — pass to patent_landscape_result
statusYes
eta_secondsYes
submitted_atYes

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds that the tool returns immediately with a job_id, estimates 180s processing, and that include_citation_graph adds significant time. No contradictions; it provides valuable behavioral context beyond annotations.

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 a single well-structured paragraph, front-loading key features, then use cases, then async workflow. No unnecessary words; every sentence adds value.

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 (async, optional graph, polling vs webhook), the description covers all essential aspects: parameters, behavior, use cases, and result retrieval. Output schema exists, so return values need not be detailed. It is complete and self-contained.

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

Parameters5/5

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

With 100% schema coverage, the description adds significant value: it explains the max_results difference versus sync mode, the effect of include_citation_graph, and the expected response time. It enriches each parameter's meaning beyond the schema.

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 it is an async extended variant of patent_landscape, listing specific capabilities (max_results up to 200, include_citation_graph). It distinguishes from its sync sibling and the polling result tool, making purpose unmistakable.

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

Usage Guidelines5/5

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

The description explicitly tells when to use async mode (e.g., for large-scale analyses) and how to retrieve results (polling or webhook). It lists concrete use cases (R&D white-space, FTO audits, etc.) and alternatives (sync variant, webhook management).

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.

Resources