A
licenseB
qualityC
maintenancePre-execution governance for AI agents. 45 MCP tools for hold queues, audit trails, risk scoring, and policy enforcement. Validates agent actions before they execute.
45
118
1
MIT
ai-governance-controls
by cody-aigov
This server provides automated AI governance controls as MCP tools to evaluate AI system prompts and deployments for safety, compliance, and security risks.
Safety Screening (
ai_safety_screen): Screen an AI system prompt for safety risks such as capability scope, authorization gaps, and data exposure, following the SAF-002 output validation control.Risk Classification (
ai_risk_classify): Classify an AI deployment’s risk tier against the EU AI Act and NIST AI RMF, following the HOC-001 risk classification control.Red Teaming (
ai_red_team): Generate an adversarial red team runbook of test cases for a system prompt, organized by attack category, following SEC-005 adversarial robustness testing (default 10, max 30 test cases).
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ai-governance-controlsScreen this system prompt for safety risks: 'You can access customer financial data.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI Governance Controls
Automatable AI governance controls as MCP tools. Paste a system prompt, describe a deployment, or submit content for review and get a structured governance report back.
Works in Claude Code, Cursor, Windsurf, Codex, and any other MCP-compatible tool.
Built on the control library from AI Governance Institute.
Available controls
Tool | Control | What it does |
| Screen a system prompt for safety risks: capability scope, authorization gaps, data exposure | |
| Classify an AI deployment's risk tier against EU AI Act and NIST AI RMF | |
| Generate a red team runbook of adversarial test cases for a system prompt |
Related MCP server: scan-your-ai-toolkit
Installation
Add this block to your MCP configuration file. No global install required.
{
"mcpServers": {
"ai-governance-controls": {
"command": "uvx",
"args": ["ai-governance-controls"]
}
}
}Configuration file locations:
Claude Code:
.claude/settings.json(project) or~/.claude/settings.json(global)Cursor:
.cursor/mcp.jsonWindsurf:
.windsurf/mcp.json
Requires uv to be installed.
Usage
Once installed, call the tools directly in conversation.
Safety screening:
Screen this system prompt for safety risks: "You are a customer service assistant with access to customer accounts. You can initiate transfers up to $10,000 on behalf of verified customers."
Risk classification:
Classify the risk tier for this deployment: "An automated resume screening system used by a Fortune 500 company to shortlist job applicants. No human reviews the shortlist before candidates are rejected."
Red teaming:
Red team this system prompt with 15 test cases: "You are a helpful assistant for a healthcare provider. You have access to patient records and can answer questions about their medical history."
How it works
Each tool runs lightweight heuristic pre-screening on your input, then returns an expert evaluation framework to your host LLM. The host LLM completes the analysis using the framework and produces a structured report. No additional API keys required.
Requirements
Any MCP-compatible AI tool (Claude Code, Cursor, Windsurf, Codex, etc.)
License
Apache 2.0. See LICENSE.
More
Full control library, self-assessment wizard, and real-time regulatory alerts at aigovernance.com.
Available Tools
3 toolsai_red_teamA
Generate adversarial test cases for an AI system prompt (SEC-005).
Produces a red team runbook: specific adversarial inputs tailored to the system prompt, organized by attack category. Returns a structured framework for the host to generate the test cases.
Args: system_prompt: The system prompt to red team. num_test_cases: Number of test cases to generate (default 10, max 30).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| system_prompt | Yes | ||
| num_test_cases | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description must cover behavior. It states the output is a 'red team runbook' and 'structured framework', but does not disclose any side effects, permissions, or rate limits. Adequate but minimal.
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?
The description is concise with two paragraphs. Front-loaded with the core purpose, followed by parameter details. Every sentence adds value, no fluff.
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?
With an output schema present, the description need not detail return values. It covers purpose, output type, and parameters effectively. Could benefit from a brief example or more detail on the runbook structure, but is largely complete for a simple tool.
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 0%, but the description includes an 'Args' section that explains both parameters: system_prompt (the prompt to test) and num_test_cases (default 10, max 30). Adds significant meaning beyond the schema's bare titles and types.
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?
Clearly specifies the verb 'Generate adversarial test cases' and the resource 'AI system prompt', with a security procedure reference (SEC-005). Distinguishes itself from siblings ai_risk_classify and ai_safety_screen by its specific red-teaming focus.
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 testing AI prompts, but lacks explicit guidance on when to use this tool versus siblings or when not to use it. No exclusions or alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ai_risk_classifyA
Classify an AI deployment's risk tier and applicable regulations (HOC-001).
Evaluates a deployment description against the HOC-001 risk classification control, referencing EU AI Act risk tiers and NIST AI RMF. Returns a structured analysis framework for the host to complete.
Args: deployment_description: Description of the AI system and how it is deployed. Include: what the system does, who uses it, what decisions it influences, what data it processes, and any human oversight in place.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| deployment_description | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.6/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the evaluation process and references regulations, and notes the return is a 'structured analysis framework for the host to complete,' implying interactive use. No annotations are present, so the description carries the full burden but does not disclose all behavioral traits (e.g., side effects, auth requirements).
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 well-structured with a clear purpose statement, explanation of the evaluation framework, and an Args section. It is reasonably concise for the complexity, though some sentences could be tightened.
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?
Given the single parameter and presence of an output schema, the description provides adequate context for the tool's purpose and input. The notion of a 'structured analysis framework' is somewhat vague, but sufficient for an agent to understand the tool's role.
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?
With 0% schema description coverage, the description compensates by providing detailed guidance on what to include in the deployment_description parameter (system purpose, users, decisions, data, oversight). This adds significant meaning beyond the schema's basic type and title.
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?
Clearly states it classifies risk tier and applicable regulations for an AI deployment, referencing specific controls (HOC-001, EU AI Act, NIST AI RMF). Does not explicitly differentiate from sibling tools like ai_red_team and ai_safety_screen, but the distinct purpose is evident.
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?
Provides guidance on when to use (evaluating deployment description against HOC-001) and what to include in the description. Lacks explicit when-not-to-use examples or alternatives, though the context of siblings might imply they are for different tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ai_safety_screenA
Screen an AI system's configuration for safety risks (SAF-001).
Evaluates a system prompt against the SAF-002 output validation control. Returns a structured analysis framework for the host to complete.
Args: system_prompt: The system prompt or configuration to screen. context: Optional deployment context (e.g. "customer-facing chatbot for a bank").
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| system_prompt | Yes | ||
| context | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it evaluates against SAF-002 and returns a framework for the host to complete, but does not detail the evaluation process, side effects, or whether it runs actual checks. No annotations were provided to supplement.
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 plus a clean Args block. Efficient but could condense further. Information is well front-loaded.
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?
Given an output schema exists, description does not need to detail return format. Covers core behavior and parameters. Could elaborate on output usage or integration with other safety tools.
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?
Description includes an Args section explaining both parameters (system_prompt and context) with practical examples, adding meaning beyond the schema which only provides titles and types. Schema coverage is effectively 100% via description.
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?
Clearly states the tool screens an AI system's configuration for safety risks (SAF-001) and evaluates a system prompt against a control. Differentiates from siblings (ai_red_team, ai_risk_classify) by focusing on prompt screening versus red teaming or risk classification.
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?
Implies usage for safety screening but does not explicitly specify when to use this tool versus siblings. Lacks 'when-not-to-use' instructions or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v0.1.0- First observed
ai_red_team - First observed
ai_risk_classify - First observed
ai_safety_screen
TDQS
A3.6/5.0
Disambiguation5/5
Each tool targets a distinct governance activity: red teaming, risk classification, and safety screening. No overlap in purpose, making them easily distinguishable for an agent.
Naming Consistency4/5
All tools use snake_case with 'ai_' prefix and a descriptive term + action. 'ai_red_team' slightly deviates as noun-noun versus verb-noun in others, but the pattern is mostly consistent.
Tool Count3/5
Three tools cover core governance areas but feel minimal for a comprehensive governance suite. The count is acceptable for a focused server but borderline low for broader coverage.
Completeness2/5
Only three of many possible AI governance controls are implemented (e.g., missing bias, privacy, explainability). Moreover, tools return analysis frameworks rather than performing actual analysis, leaving significant gaps.
Maintenance
ActivityInactive
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
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