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record_risk

Record new or reviewed AI risks in an EU AI Act Article 9 register, with source, harm, measures, residual assessment, and test results.

Instructions

Append one entry to the risk register (EU AI Act Art. 9). The same risk_id again is a review or a re-estimate; the register shows the latest state and how long since the last review. source is intended_use, foreseeable_misuse or post_market (9(2)(a) to (c)); harm is health, safety or fundamental_rights; measure_kind is eliminate, mitigate or inform (9(5)); residual plus residual_acceptable is the 9(5) judgement; tests lists {metric, threshold, observed, passed} against a threshold defined before the test (9(8)); refers_to lists ledger seqs and each must exist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
harmYes
actorYes
testsNo
hazardYes
sourceYes
statusNoopen
measureNo
risk_idYes
evidenceNo
residualNo
severityNomedium
refers_toNo
likelihoodNomedium
measure_kindNo
residual_acceptableNo
affects_vulnerable_groupsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.44.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden, and it does so well: it discloses the append mutation, the review/re-estimate behavior for repeated risk_id, the latest-state/time-since-review behavior, and the referential constraint that refers_to entries must exist. It omits auth, return, and failure details, but provides substantial behavioral context.

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

Conciseness4/5

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

The description is dense and front-loaded with its core purpose; every clause carries relevant information. It is somewhat run-on and hard to parse quickly, but there is no wasted content.

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?

For a 16-parameter tool with no annotations and no output schema, the description is comprehensive for the EU Act domain it covers, but it leaves required parameters such as hazard and actor undocumented and says nothing about return values or error behavior. It is functional but not fully complete.

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 description coverage is 0%, so the description must compensate. It adds meaning for source, harm, measure_kind, residual/residual_acceptable, tests, and refers_to. However, several parameters including required hazard and actor are never explained, and fields like severity, likelihood, evidence, status, and measure receive no semantic guidance.

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?

Opens with a specific verb and resource: 'Append one entry to the risk register (EU AI Act Art. 9)' and further explains that reusing risk_id turns the entry into a review or re-estimate. This clearly distinguishes it from read-only siblings like risk_register and from unrelated record tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for appending risk register entries or reviewing existing risk_id values, but it does not explicitly name alternatives or state when not to use this tool. It gives strong domain context but leaves sibling-tool routing mostly implicit.

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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