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Mipiti
by Mipiti

auto_map_controls

LLM-map a model's existing controls to a framework's requirements. Requires PRO tier. Mutating: writes control-to-requirement mappings. Runs as a background job (typically 20-45s); this tool waits for completion and returns the result.

Instructions

LLM-map a model's existing controls to a framework's requirements. Requires PRO tier. Mutating: writes control-to-requirement mappings. Runs as a background job (typically 20-45s); this tool waits for completion and returns the result.

Sits between the manual map_control_to_requirement (one mapping at a time) and the full auto_remediate_compliance loop (which also excludes non-applicable requirements and proposes new entities for remaining gaps). auto_map_controls only creates mappings from controls that already exist — it never adds or excludes entities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
control_idNoOptional single control ID to map; omit to map all of the model's controls.
framework_idYesID of the compliance framework.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so description carries full burden. It discloses mutation (writes mappings), PRO tier requirement, background job with typical duration (20-45s), wait-for-completion behavior, and scope limitation. This is exemplary transparency.

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?

Description is well-structured, front-loaded with core purpose, then key constraints and expectations. No redundant filler; every sentence adds value.

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 output schema exists, description needn't detail return values. It covers purpose, usage boundaries, parameters, behavioral nuances, and prerequisites. Completely adequate for a complex 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 75%, missing description for server_version. Description adds context on control_id (optional, omit to map all) and clarifies framework/model IDs purpose. It does not compensate for server_version, but the param role might be evident from context.

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?

Description clearly states it maps a model's existing controls to a framework's requirements, distinguishing it from manual mapping and full auto-remediation. It also specifies it only creates mappings, never adds or excludes entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly positions between manual map_control_to_requirement and auto_remediate_compliance, with clear differences. Also states when to use (when you want to map existing controls) and implicitly when not (when exclusions/additions are needed).

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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