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mcp-knowledge

by io.github.getgapup

patent_ownership_audit

Read-onlyIdempotent

Audits patent ownership for employees or contractors, identifying gaps where inventors may not have properly assigned patent rights to the company. Designed for CHROs to ensure IP compliance and mitigate legal risks. Inputs: employee/contractor names or IDs, optional date range. Outputs: list of patents, ownership status, flagged gaps, and assignment details. Sources: USPTO PatFT and EPO Espacenet public records. Keywords: patent audit, IP compliance, employee inventions, contractor agreements, CHRO.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
dateRangeNoOptional date range for patent filings
employeeIdsNoList of employee or contractor IDs (optional if names provided)
employeeNamesYesList of employee or contractor full names to audit

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
gapsNo
statusYes
patentsNo
sourcesNo
warningsNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint. The description adds value by disclosing data sources (USPTO PatFT, EPO Espacenet) and outputs (list, ownership status, flagged gaps, assignment details). It reveals the tool's external data reliance and scope, enhancing transparency 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 of five sentences. It front-loads the core purpose, then covers user, inputs, outputs, sources, and keywords. No superfluous information; every sentence contributes meaningfully.

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

Completeness4/5

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

Given the presence of an output schema, the description need not detail return values. It covers purpose, user, inputs, outputs, and sources. Missing aspects like error handling or data freshness are minor; overall the description equips the agent with sufficient operational context.

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%, so the schema already documents parameters. The description reiterates that inputs are employee/contractor names or IDs with optional date range, but does not add technical detail beyond schema. Credit for clarifying the purpose of inputs in context, but overall minimal added value.

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 the verb 'audits' and the resource 'patent ownership' for employees/contractors. It identifies gaps in patent rights assignment. The tool is distinct from sibling tools like patent_landscape or ip_employee_invention_tracker, and the description includes 'patent audit' as a keyword, making purpose unambiguous.

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 specifies the primary user ('designed for CHROs') and context ('ensure IP compliance and mitigate legal risks'). It lists inputs (names/IDs, optional date range) and provides output expectations. While it doesn't explicitly state when not to use or name alternatives, the context is clear enough for appropriate selection.

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

All mcp-knowledge toolsInspect
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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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