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Systemores Cloud MCP Server

Systemores Cloud MCP Server

by bobcar0

Export Regulatory Compliance Dossier

systemores_export_dossier
Read-onlyIdempotent

Exports a completed audit session's graph audit, cohort simulation, chaos test, and trace findings into formal compliance dossiers for EU AI Act, NIST AI RMF, EEOC/ECOA, and SOC 2 AI Addendums.

Instructions

Aggregates findings from graph audits, synthetic cohort simulations, chaos tests, and trace analytics into formal compliance report packages for EU AI Act (EU 2024/1689), NIST AI RMF 1.0, EEOC/ECOA, and SOC 2 AI Addendums.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "auditSessionId": "<auditSessionId>"
    },
    "name": "systemores_export_dossier"
  }
}

Input Schema

NameRequiredDescriptionDefault
companyNameNoLegal entity or client name for dossier attribution.Enterprise Client
auditSessionIdYesUnique identifier for the completed audit session.
targetFrameworksNoComma-separated list of regulatory frameworks to generate formal dossier sections for: 'eu_ai_act', 'nist_ai_rmf', 'eeoc_ecoa', 'soc2_ai'. Defaults to all if omitted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.3

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered structurally. The description adds real value by disclosing which four upstream data sources are combined, which is behavior an agent cannot see in the schema. However, it never says whether the dossier is returned inline, written to a file path, or how large/slow the aggregation is — meaningful gaps for an 'export' operation even with annotations 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?

A single sentence that front-loads the verb and the aggregation behavior, then lists sources and frameworks. Every clause carries information an agent would otherwise have to infer; there is no filler or repetition of the title.

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?

There is no output schema, so the description carries the burden of explaining what an 'export' produces, and it does not: no mention of output format (PDF/JSON bundle), delivery mechanism, or whether the call is synchronous for a potentially expensive aggregation across four data sources. Given the tool's complexity and cross-source aggregation, an agent is left underspecified on the most consequential aspect — the returned artifact.

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 would be 3, but the description goes further by spelling out the regulatory frameworks in full (EU AI Act 2024/1689, NIST AI RMF 1.0, EEOC/ECOA, SOC 2 AI Addendums), which lets an agent map human-readable regulation names to the 'eu_ai_act'/'nist_ai_rmf' style values in targetFrameworks. It does not clarify companyName's attribution effect or confirm the 'all frameworks' default beyond what the schema says.

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 states a concrete action (aggregating findings into formal compliance report packages) and enumerates the input sources — graph audits, synthetic cohort simulations, chaos tests, trace analytics — plus the target regulations. This clearly distinguishes it from siblings like systemores_audit_agent_graph or systemores_stress_test_agent, which produce findings rather than package them. The verb 'export' appears only in the name/title, not the description body, which slightly weakens the statement of what the caller actually receives.

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?

Usage is implied rather than stated: the required auditSessionId is described in the schema as a 'completed audit session', suggesting this runs after audits finish. There is no explicit when-to-use/when-not-to-use guidance and no named alternative for callers who want a single framework's raw findings instead of a dossier. An agent can infer the sequencing but must guess at exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.