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safety_violation_incident_logger

Read-onlyIdempotent

Logs AI safety violations for compliance reporting, targeting risk management personas. Accepts incident details such as violation type, severity, description, and timestamp. Returns structured data with compliance categorization based on NIST AI RMF guidelines. Ideal for automated incident tracking and regulatory reporting workflows.

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.
metadataNo
severityYes
timestampYes
descriptionYes
violationTypeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
incidentIdNo
nistReferenceNo
complianceCategoryNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.6/5.0
Behavior1/5

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

The description says 'Logs AI safety violations', which implies a write operation that creates a record. This directly contradicts the annotation readOnlyHint=true, which claims the operation is read-only. Additionally, idempotentHint=true conflicts with the typical behavior of logging a new incident each time. The description offers no clarification of these inconsistencies, making this a serious annotation contradiction.

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 three sentences, with the first stating the main purpose and the third adding output and use context. However, the second sentence ('Accepts incident details such as violation type, severity, description, and timestamp') largely restates the schema fields and adds no value. It's not overly verbose but contains redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool description provides basic information about input and output but omits critical details like the side effects of logging (especially given the readOnlyHint contradiction), the meaning of NIST AI RMF categorization, and how it relates to sibling tools. While an output schema exists, it doesn't compensate for the misleading safety profile and lack of usage guidance.

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

Parameters2/5

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

The schema has six parameters but only 17% description coverage (only async has a description). The tool description merely lists 'violation type, severity, description, and timestamp' without adding formats, constraints, relationships, or meaning beyond their names. It doesn't explain the purpose of async or metadata, so it fails to compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Logs AI safety violations for compliance reporting', identifying the verb (logs) and resource (AI safety violations). It also mentions the return of structured data with NIST AI RMF categorization, which clarifies scope. However, it doesn't explicitly differentiate from closely related sibling tools like incident_response_evidence_collector or safety_guardrail_breach_analyzer, so it falls short of a 5.

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 says it's 'Ideal for automated incident tracking and regulatory reporting workflows', providing some context for when to use it. But it gives no exclusions, alternatives, or conditions for selection over sibling tools. The guidance is implied rather than explicit, so it doesn't earn more than a 3.

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.