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Mipiti
by Mipiti

export_threat_model_archive

Export a complete JSON audit archive of a threat model, including versions, controls, findings, and verifiable signatures. The archive supports independent verification via OIDC JWTs and workspace keys.

Instructions

Export the self-contained JSON audit archive for a threat model.

The archive carries every version, controls, assertions (with CI Tier 1/Tier 2 verdicts and attested flags), findings, risk acceptances, assumption overrides, attestations, and instance sufficiency signatures. Independently verifiable — CI OIDC JWTs verify against the issuer's public JWKS, workspace signatures against the workspace's published key, and sufficiency signatures against the origin instance's key (via the target's trusted_signers table).

The backend renders the archive as an async job (the same cross-model assurance compute that motivated PDF/HTML to migrate away from synchronous rendering). This tool kicks off the job, polls for completion via _await_backend_job (reporting progress), then fetches and decodes the JSON envelope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model to export.
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 provided, but the description details the async job process, polling via _await_backend_job, progress reporting, and verification steps (CI OIDC JWTs, workspace signatures, sufficiency signatures). This adds substantial behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative but somewhat lengthy, covering purpose, contents, async workflow, and verification. It could be more concise, but it is well-structured with front-loaded purpose.

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

Completeness3/5

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

Given the complexity (async job, multiple verification details) and presence of an output schema, the description covers the archive contents and process well. However, it lacks details on potential errors or the exact structure of the JSON envelope, making it moderately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 2 parameters (model_id, server_version) with 50% description coverage. The description does not add meaning beyond the schema for server_version, and only minimally for model_id. It fails to explain the server_version parameter.

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 'Export the self-contained JSON audit archive for a threat model', specifying a specific verb and resource, and distinguishes it from siblings like export_threat_model and import_threat_model_archive.

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to use this tool versus alternatives like export_threat_model. It does not provide when-to-use or when-not-to-use context.

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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