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Compliance Posture

compliance
Read-onlyIdempotent

Scans local MCP configurations and Docker images, maps findings to 47 security controls across OWASP, MITRE ATLAS, and NIST AI RMF frameworks, and returns per-control pass/warning/fail status with overall compliance score.

Instructions

Get OWASP LLM Top 10 / OWASP MCP Top 10 / MITRE ATLAS / NIST AI RMF compliance posture.

    Scans local MCP configurations, maps findings to 47 security controls
    across four AI security frameworks, and returns per-control
    pass/warning/fail status with an overall compliance score.

    Args:
        config_path: Path to a specific MCP config directory.
                     If not provided, auto-discovers all local agent configs.
        image: Docker image reference to scan (e.g. "nginx:1.25").

    Returns:
        JSON with overall_score (0-100), overall_status (pass/warning/fail),
        and per-control details for OWASP LLM Top 10 (10 controls),
        OWASP MCP Top 10 (10 controls), MITRE ATLAS (13 techniques),
        and NIST AI RMF (14 subcategories).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
config_pathNoPath to MCP client config directory. Auto-discovers all if omitted.
imageNoDocker image to scan, e.g. 'nginx:1.25'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate readOnly and idempotent behavior. The description adds that it scans MCP configurations, maps to four frameworks, and returns a JSON with overall score and per-control details. This provides useful behavioral context beyond the annotations.

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 clear sections for purpose, arguments, and returns. It is comprehensive without being overly verbose, though the Args and Returns sections somewhat duplicate schema and output schema information.

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

Completeness5/5

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

Given the tool has an output schema and 100% schema coverage, the description provides complete context: it explains the scan scope, mapping details, and output structure. It also covers auto-discovery behavior for the config_path parameter.

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%, with both parameters well-described in the schema. The description restates the parameters and their purpose but does not add significant new semantics. At baseline 3, this is adequate.

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?

The description explicitly states the tool returns compliance posture for OWASP LLM Top 10, OWASP MCP Top 10, MITRE ATLAS, and NIST AI RMF frameworks. It also explains that it scans MCP configurations and maps findings to 47 controls, which clearly distinguishes it from sibling tools like cis_benchmark or code_scan.

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?

The description names specific frameworks and mentions scanning MCP configs, implying its use case. However, it does not explicitly state when not to use it or compare it to alternatives like cis_benchmark or code_scan. The context is clear but lacks exclusions.

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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