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FINOS AI Governance MCP Server

FINOS AI Governance MCP Server

Official
by finos
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Framework

get_framework

Retrieve complete content for a specific AI governance framework by identifier to enable compliance analysis, requirement extraction, and regulatory guidance.

Instructions

Get the complete content of a specific AI governance framework.

Use this tool to retrieve detailed framework content for compliance analysis, requirement extraction, or regulatory guidance. The content includes structured requirements, controls, and implementation guidance.

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": {
      "framework_id": "<framework_id>"
    },
    "name": "get_framework"
  }
}

Input Schema

NameRequiredDescriptionDefault
framework_idYesFramework identifier from list_frameworks() (e.g. nist-ai-600-1).

Output Schema

NameRequiredDescriptionDefault
titleYes
contentYes
entry_countYes
framework_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full disclosure burden. It does useful work by stating that the payload contains structured requirements, controls, and implementation guidance, but it says nothing about payload size, permission requirements, or what happens when a framework_id is unknown — meaningful gaps for a content-retrieval tool.

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?

Two sentences, front-loaded with the operation and followed by usage contexts; no sentence is redundant or padded. Every clause either states what is returned or when to reach for it.

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 details need not be in the description, and the one required parameter is fully documented in the schema. What is missing is guidance on large payloads or invalid identifiers, which would matter for an agent fetching full framework documents.

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 coverage is 100% and the single framework_id property already documents its origin (list_frameworks()) and an example value. The description adds no parameter 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?

States a specific verb and resource — 'Get the complete content of a specific AI governance framework' — and the word 'complete' plus 'specific' implicitly distinguishes it from list/search siblings. It stops short of naming which sibling to use alternatively, so an agent must infer that list_frameworks and search_frameworks serve discovery rather than retrieval.

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?

Gives concrete when-to-use contexts (compliance analysis, requirement extraction, regulatory guidance), which is real selection guidance rather than a restatement of the name. There is no explicit when-not or named alternative, so it is clear context without routing rules.

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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