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Glama

Kirk — Unsupervised Structural Change Detection

Verify Kirk Engine Identity

kirk_verify_engine
Read-only

Verify sealed engine identity — returns the sha256 of the running scoring binary. Also serves as a liveness probe against the sealed backend.

Purpose: Attest which Kirk build is currently serving scoring calls. Response carries the sealed engine sha (kirk_version) that will stamp any subsequent kirk_score_* result. Secondary role: a cheap liveness probe for callers wiring up MCP for the first time.

Use when: You want to record engine sha in your own provenance log before capturing scoring output, or you want a cheap liveness check ahead of a larger validation batch.

Do not use when: You want a scoring result — this returns identity/liveness only, no entropies.

Capability class(es): C5 (cryptographic attestation of engine identity).

Path fit: Validation via MCP (this tool). Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options.

Cost: 0 IU. Free tool. For agent-driven callers, the _cost envelope still reports iu_this_call=0 and the running session totals.

Returns: Dict with status, engine, env, and kirk_version (the sealed .so sha). A non-2xx response raises; caller sees a clean MCP tool error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and destructiveHint=false; the description adds substantial context: it serves as a liveness probe, costs 0 IU, returns a dictionary with specific fields, and non-2xx responses raise a clean MCP error. This goes far beyond the annotations and prepares the caller for observable behavior.

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 (Purpose, Use when, Do not use when, Capability class, Path fit, Cost, Returns). It is somewhat wordy, but every section adds meaningful context for a tool with no parameters, so it earns its length.

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?

For a zero-parameter tool with an output schema, the description is exceptionally complete: it covers the tool's role, return fields, cost, error behavior, and relationship to the broader system. There is no missing context needed for correct invocation.

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?

The tool has zero parameters, so the description doesn't need to explain parameters. The baseline for zero params is 4 per the rubric, and the description appropriately focuses on the response structure and use cases, making the parameter absence self-evident.

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 states the specific verb+resource: 'Verify sealed engine identity — returns the sha256 of the running scoring binary.' It clearly distinguishes itself from sibling tools by explicitly noting it is not a scoring tool and returns identity/liveness only, not entropies.

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 'Use when' and 'Do not use when' sections, and even mentions the in-process alternative for production. This gives the agent clear guidance on when to select this tool versus scoring tools.

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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TDQS

A4.7/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose: billing (checkout/show/usage), scoring (single/batch/legacy/synthetic/random), utility (health/models/bulk_howto/render/research). Descriptions are detailed enough to prevent confusion.

Naming Consistency5/5

All tools follow a consistent 'kirk_<domain>_<verb>' pattern (e.g., kirk_billing_checkout, kirk_score_book). No mixing of conventions or unpredictable names.

Tool Count5/5

12 tools is well within the optimal range. Each tool serves a specific need without redundancy, covering scoring, billing, health, models, and research.

Completeness5/5

The tool surface covers the full workflow: scoring (single/batch/legacy/synthetic/research), billing (checkout/balance/usage), health checking, model listing, tensor prep, and bulk client generation. No obvious gaps for the intended domain.