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Mipiti
by Mipiti

assess_model

Run a deterministic assurance assessment over a threat model, evaluating control objectives from implementation status and providing summary counts and progressive metrics.

Instructions

Run the deterministic assurance assessment over a threat model. Read-only — no LLM calls, no mutation.

Evaluates each control objective from its controls' implementation status and returns summary counts (mitigated / at_risk / unassessed) plus progressive metrics (defined / implemented / verified). For LLM-based reasoning about which COs are under-covered and what controls to add, use check_control_gaps instead.

Use summary_only=True to get just the counts without per-CO assessments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax control objectives to return (0 = all).
offsetNoSkip the first N control objectives.
statusNoOptional filter — "mitigated", "at_risk", or "unassessed".
model_idYesID of the threat model to assess.
summary_onlyNoIf True, return only summary counts (no per-CO details).
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are present, so the description carries the full burden. It discloses that the tool is read-only, makes no LLM calls, and performs no mutation, and explains the deterministic evaluation logic. This covers the core behavioral traits, though it omits potential authorization or error details.

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 compact—three sentences—and front-loaded with the core purpose. Each sentence contributes: behavior, differentiation, and a usage tip, with no unnecessary filler.

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 output schema exists (has output schema: true), the description need not detail return values. It covers the tool's purpose, read-only nature, deterministic behavior, and alternative use case. The description is sufficient for a 6-parameter tool with high schema coverage.

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 description coverage is 83%, so the schema already documents most parameters. The description adds value by explaining the summary_only parameter (returns only counts) and clarifying that statuses like mitigated/at_risk/unassessed map to the evaluation output, aiding the status parameter's use.

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 clearly states the tool's action ('Run the deterministic assurance assessment over a threat model') and resource ('threat model'), with a specific verb. It also explicitly differentiates from the sibling tool check_control_gaps, making its unique purpose unmistakable.

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?

Provides explicit when-to-use guidance for deterministic assessment and directs users to check_control_gaps for LLM-based reasoning about control gaps. The read-only note also signals it is appropriate for safe, non-mutating evaluation.

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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