gapup-mcp

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

Name	Required	Description
`mode`	No	search / landscape / lookup. Default: "search"
`query`	Yes	Keywords, company/inventor name, or patent number (e.g. "machine learning", "Tesla Inc")
`date_to`	No	ISO date YYYY-MM-DD — latest filing date
`date_from`	No	ISO date YYYY-MM-DD — earliest filing date
`max_results`	No	Max patents to return (5-200). Default: 20
`jurisdictions`	No	Jurisdictions to include. Default: ["US","EP","WO"]
`include_citation_graph`	No	If 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

Name	Required	Description
`job_id`	Yes	Unique job identifier — pass to patent_landscape_result
`status`	Yes
`eta_seconds`	Yes
`submitted_at`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

Description discloses async nature, immediate job_id return (<300ms), eta_seconds (~180s) for polling, and option for webhook callbacks. Annotation readOnlyHint=true aligns with read operation. Adds behavioral context beyond annotations 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?

Front-loaded with key purpose, then lists features, usage, and options in a logical order. No redundant sentences; each sentence adds substantive information. Appropriate length for complexity.

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?

Covers all essential aspects: async nature, result retrieval, eta, webhook registration, use cases, and parameter highlights. Output schema exists to detail return values, so description doesn't need to repeat that. Complete for a complex async tool.

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?

Schema coverage is 100%, baseline 3. Description adds value by explaining max_results limit (200 vs 50 sync) and citation graph enrichment (2-level). Other parameters like mode and date ranges are adequately described in schema, so minimal additional meaning but still enhances understanding.

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?

Description clearly states it's an async variant of patent_landscape, specifies max_results up to 200 and optional citation graph. Distinguishes from sibling tools 'patent_landscape' (sync) and 'patent_landscape_result' (polling) by mentioning async behavior and alternative use cases.

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?

Explicitly lists use cases: deep R&D white-space analysis, FTO audits, VC due diligence, large-scale competitor portfolio analysis. Contrasts with sync variant and provides guidance on polling vs webhook for result retrieval.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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