Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

talent_contract_risk_mapper

Read-onlyIdempotent

For CHROs: analyzes employee contracts for non-compete, IP assignment, and confidentiality clauses, comparing against state labor laws and jurisdiction-specific precedents. Returns risk levels, conflicting statutes, and suggested revisions. Uses USPTO PatFT, CourtListener, and EUR-Lex for legal cross-referencing. Ideal for contract reviews, compliance audits, or policy updates.

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.
jurisdictionYesState or country jurisdiction (e.g., 'California', 'Germany')
contract_textYesFull text of the employee contract or clause section to analyze
employee_roleNoJob title or role classification (e.g., 'Software Engineer', 'Executive')
effective_dateNoContract effective date (YYYY-MM-DD)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
risk_summaryNo
suggested_revisionsNo
conflicting_statutesNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, and open-world behavior; the description adds context about external sources (USPTO, CourtListener, EUR-Lex) and output contents (risk levels, conflicting statutes, revisions), 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?

Three focused sentences: target user, core analysis with clauses and data sources, and use cases. No fluff, front-loaded with purpose, and well-structured.

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, annotations (readOnly, openWorld, idempotent), full schema coverage, and output schema existence, the description provides sufficient context for usage and expectations.

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 descriptions already cover all 5 parameters with high detail (100% coverage). The description does not add new meaning beyond what's in the schema, so baseline 3 applies.

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 employee contracts for specific clauses (non-compete, IP assignment, confidentiality) and compares against labor laws, returning risk levels and suggestions. This specificity distinguishes it from siblings like contract_risk_scanner.

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 mentions ideal use cases (contract reviews, compliance audits, policy updates) and targets CHROs, but does not explicitly contrast with sibling tools or state when not to use.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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