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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 already declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds meaningful behavioral context: it uses external legal databases (USPTO PatFT, CourtListener, EUR-Lex) for cross-referencing and describes the return structure. This goes beyond the annotations and helps manage expectations about external dependencies and output format.

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 three concise sentences, front-loaded with the target audience ('For CHROs') and the core action. It avoids redundant wording and covers purpose, outputs, and use cases efficiently. Every sentence earns its place.

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?

The description covers the tool's purpose, intended use cases, output types, and external data sources. Given that an output schema exists, the description does not need to detail return values further. It is comprehensive enough for a complex tool with 5 parameters and 2 required fields, and the annotations handle safety and idempotency.

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?

The input schema already provides 100% description coverage for all parameters, including defaults and types. The tool description adds minimal parameter-specific guidance, only implicitly referencing contract_text and jurisdiction in the purpose. This aligns with the baseline 3 for high schema coverage, as the description does not add substantial new meaning beyond the schema.

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 uses a specific verb ('analyzes') and resource ('employee contracts'), explicitly identifies the three clause types (non-compete, IP assignment, confidentiality), and states the outputs (risk levels, conflicting statutes, suggested revisions). It clearly distinguishes itself from sibling tools like contract_risk_scanner by focusing on talent contracts and legal cross-referencing.

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 provides clear context for when to use the tool: 'Ideal for contract reviews, compliance audits, or policy updates' and targets CHROs. However, it does not explicitly mention alternatives or state when not to use it, so it falls short of the top score.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.