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Mipiti
by Mipiti

import_functional_tests

Import existing tests from your codebase to associate them with functional objectives, verifying associations and reporting rejected mappings.

Instructions

Register existing tests from the codebase against a model's functional objectives, so tests you already have count toward functional conformance — not only Mipiti-specified tests.

Scan the repo's test suite and pass the tests here. Optionally associate each with the objective ids it covers (from list_functional_objectives); the platform verifies each association is applicable before accepting it and returns any it rejected under rejected_mappings. A test with no (or a rejected) association is still imported, unmapped, so it can be associated later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
tests_jsonYesA JSON array of test objects. Each object supports ``test_name``, ``file_path``, ``framework``, ``description``, ``status`` (not_implemented | implemented | verified — an operator claim; an independent CI run is what verifies it), and ``functional_objective_ids`` (list of objective ids the test covers). At least ``test_name`` or ``description`` is required per test; the rest are optional.
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 exist, so description carries full burden. It discloses that the platform verifies objective id associations and returns rejected mappings, and that tests with no/rejected mapping are still imported unmapped. This explains key behavioral traits without contradiction.

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 a single paragraph that front-loads the main purpose and then elaborates on details. It is concise enough for an AI to grasp quickly, though it could be broken into bullet points for clarity.

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 complexity of importing tests with optional associations and verification, the description covers the entire process, including fallback behavior. An output schema exists, so return values need not be explained here. The description is complete for effective tool selection and invocation.

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 67% (model_id and tests_json described, server_version not). Description adds significant meaning to tests_json by explaining internal structure, field requirements, and status semantics. However, it does not add value for model_id or server_version beyond the schema.

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 registers existing tests against functional objectives, using a specific verb (import) and resource (tests). It distinguishes from related tools like add_functional_test and associate_functional_test by focusing on bulk import from codebase.

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?

Provides clear context for when to use (to count existing tests toward functional conformance) and mentions the optional association with objective ids. Does not explicitly state when not to use or name sibling alternatives, but the implied use case is distinct.

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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