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jiawei686

jev-legal-clause-mcp

by jiawei686

flag_clauses

Scan contract clauses to get calibrated risk probabilities for 10 categories, an overall risk score, and a human review recommendation. Identifies legal red flags before signing.

Instructions

Legal-risk clause flagger powered by Jev (System One decision model). Given a contract excerpt, returns calibrated presence probabilities for 10 default risk categories (indemnification / non-compete / confidentiality / termination penalty / auto-renewal / liability cap / IP assignment / governing law / payment terms / personal guarantee) plus an overall risk score and a review recommendation. This is a triage aid — NOT a legal opinion. Always have a human review before signing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe contract excerpt to scan (a clause, section, or whole paragraph).
typesNoOptional custom risk categories to scan. Defaults to the 10 built-in types.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations and no output schema, the description carries the full disclosure burden. It does well by stating outputs (probabilities, risk score, recommendation), the underlying model (Jev System One), and the operational caveat that this is triage, not legal advice. It could mention read-only behavior or input limits, but the key behavioral boundaries are present.

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?

Four short, purposeful sentences. The first sentence identifies the tool, the second explains inputs and outputs, and the last two convey the critical limitation. No filler or redundant restatement.

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?

For a triage/analysis tool with no output schema or annotations, the description adequately covers input, output categories, risk score, recommendation, and limitations. It stops short of specifying the exact response format or error conditions, but an agent has enough to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by enumerating the 10 default risk categories, which gives an agent concrete knowledge of what 'types' accepts even though no enum is provided. This goes beyond the schema's generic 'built-in types' phrasing.

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?

States a specific verb ('flag') and resource ('clauses'), and describes exactly what it does: accepts a contract excerpt and returns calibrated probabilities for 10 named risk categories, an overall risk score, and a recommendation. The output behavior is specific enough that an agent can distinguish this from generic text-analysis tools even without siblings.

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?

Clearly frames the tool as a triage aid to be used before signing, and explicitly warns it is not a legal opinion and requires human review. It lacks explicit when-not-to-use conditions or named alternatives, but with no sibling tools the context is sufficient.

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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