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AiAgentKarl

patent-intelligence-mcp-server

get_patent_landscape

Analyze patent trends, top players, CPC distribution, geographic activity, and competitive intensity for a technology area to support strategic research, due diligence, and market analysis.

Instructions

Erstellt eine umfassende Technologie-Landschaft für einen Bereich.

Kombiniert Trend-Analyse, Top-Player, CPC-Klassifikationsverteilung, geografische Verteilung, Aktivitäts-Heatmap und Wettbewerbsintensität. Ideal für strategische Patent-Recherche, Due Diligence und Marktanalysen.

Args: query: Technologie-Suchbegriff, z.B. "solid state battery", "gene therapy" years: Analysezeitraum in Jahren (1-20, Standard: 5) top_n: Anzahl Top-Unternehmen (1-20, Standard: 10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
yearsNo
top_nNo
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It describes the output components but fails to disclose any behavioral traits such as side effects, auth needs, rate limits, or how results are returned. This is insufficient for a mutation-like tool (creating a landscape).

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 concise with two main sentences plus the Args list. It is front-loaded with the purpose and then lists components. No wasted words, though it could be slightly more streamlined.

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

Completeness3/5

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

Given the complexity of the tool (multiple analyses combined) and no output schema, the description should explain the return format or structure of the landscape. It lists components but does not describe how they are presented. This is a gap for agent invocation.

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?

The schema coverage is 0%, but the description's Args section provides detailed semantics: query includes examples, years specifies range 1-20 with default 5, top_n specifies range 1-20 with default 10. This fully compensates for the lack of schema descriptions, adding constraints and examples.

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 that the tool creates a comprehensive technology landscape for a field, listing specific components like trend analysis, top players, CPC classification, etc. This provides a specific verb+resource combination that distinguishes it from siblings, though explicit differentiation is not given.

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

Usage Guidelines3/5

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

The description mentions it is ideal for strategic patent research, due diligence, and market analysis, providing some context. However, it does not explicitly state when to use this tool versus alternatives like analyze_patent_landscape or search_patents, nor does it include exclusions.

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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