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mcp-knowledge

by io.github.getgapup

ip_contract_clause_extractor

Read-onlyIdempotent

For CHRO use: analyzes employment contract text to identify and extract IP-related clauses such as invention assignment, confidentiality, non-compete, and patent rights. Returns structured data with clause types, risk levels, and relevant legal context. Ideal for contract review workflows, compliance checks, and IP protection strategy. Sources: USPTO PatFT and EPO Espacenet public datasets. Keywords: employment contract, IP clause, invention assignment, confidentiality agreement, non-compete, patent rights, CHRO tool.

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.
contractTextYesFull text of the employment contract to analyze
jurisdictionNoCountry/state jurisdiction for legal context (e.g., 'US-CA', 'DE')
includeContextNoWhether to include legal context for each clause

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
clausesYes
sourcesNo
summaryYes
warningsYes

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already mark the tool as readOnly, openWorld, and idempotent. The description adds behavioral context by stating returns structured data with clause types, risk levels, and legal context, and mentions data sources (USPTO, EPO). However, the sources seem misplaced for contract analysis, slightly reducing credibility.

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

Conciseness3/5

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

The description is front-loaded with 'For CHRO use' but contains extraneous details like data sources (USPTO, EPO) that are incongruent with contract analysis, and a list of keywords. These dilute conciseness and could be removed or corrected.

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 availability of an output schema, the description covers the core functionality and typical use cases. However, it does not explain the output schema's structure or how to interpret risk levels, and the mention of unrelated patent data sources may confuse users.

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 baseline is 3. The description reiterates the main parameter (contractText) and hints at includeContext, but does not add new meaning beyond what the schema already provides. The jurisdiction parameter is mentioned in examples but not elaborated.

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 tool analyzes employment contract text to identify IP-related clauses, listing specific clause types (invention assignment, confidentiality, non-compete) and use cases. It distinguishes from siblings by focusing on IP clauses in employment contracts, which is specific and not overlapping with general clause extractors.

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 explicitly targets CHRO use and mentions common workflows (contract review, compliance checks, IP protection strategy). It does not provide alternative tools or when-not-to-use guidance, but the context is clear enough for the intended audience.

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.

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