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FINOS AI Governance MCP Server

FINOS AI Governance MCP Server

Official
by finos
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Mitigation

get_mitigation

Retrieve complete AI governance mitigation strategy details, including security controls, implementation steps, monitoring procedures, and effectiveness measures for risk reduction.

Instructions

Get the complete content of a specific AI governance mitigation strategy for risk control implementation.

Use this tool to retrieve detailed mitigation instructions including security controls, implementation steps, monitoring procedures, and effectiveness measures. Critical for security implementation, compliance remediation, and risk reduction planning.

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": {
      "mitigation_id": "<mitigation_id>"
    },
    "name": "get_mitigation"
  }
}

Input Schema

NameRequiredDescriptionDefault
mitigation_idYesMitigation document identifier from list_mitigations(), e.g. 13_providing-citations-and-source-traceability-for-ai-generated-information; the short form the documents use, mi-13, ct-13 or 13, is accepted too.

Output Schema

NameRequiredDescriptionDefault
titleYes
contentYes
sectionsYes
document_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral load; it does disclose the returned content types (security controls, implementation steps, monitoring procedures, effectiveness measures), which is useful. However, it says nothing about read-only semantics, authentication/permission needs, or behavior when the identifier is unknown or ambiguous, leaving meaningful gaps.

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?

Two sentences, front-loaded with the core action and followed by content and use-case detail. The trailing list of use cases ('security implementation, compliance remediation, risk reduction planning') is somewhat padded but not harmful.

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?

An output schema exists, so return-value structure needn't be explained, and the description adequately covers what the tool fetches and for what purposes. The main shortfall is the absence of lookup-failure or permission behavior, but for a simple single-parameter read tool this is largely sufficient.

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 100% and the single mitigation_id parameter is fully documented in-schema, including accepted short forms and its origin from list_mitigations(). The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

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 names a specific verb ('get') and resource ('AI governance mitigation strategy') plus scope ('complete content'), which distinguishes it from siblings such as get_framework and get_risk. It stops short of explicitly contrasting itself with those siblings, so it is clear but not maximally differentiated.

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?

It offers usage scenarios ('security implementation, compliance remediation, and risk reduction planning'), which implies when to reach for it, but gives no exclusions and never says when to prefer list_mitigations or search_frameworks instead. The only pointer to list_mitigations lives in the schema description rather than the tool description.

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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