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by uk.co.21dle.testgraph

Affirm the current subject type

affirm_subject_classification
Idempotent

Submit one independent AI model's evidence-backed agreement with the subject's existing provisional type. Two distinct model identities agreeing on that type automatically confirm and lock it without moving the subject. Use this when the current type is already correct and no stricter descendant is justified.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYes
evidenceYes
subject_idYes
source_modelYesStable model identity, not the client application name.
evidence_fingerprintNo

Schema Changelog

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

  1. Changed2 schema fields changed
    • removedInput schema / properties / version_check
      Removed value: -{
-  "description": "Required live deployment token. Call get_server_info immediately before this write and pass write_version_token unchanged. Stale or missing tokens are rejected before any write occurs.",
-  "maxLength": 64,
-  "minLength": 64,
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "subject_id",
-  "source_model",
-  "reason",
-  "evidence",
-  "version_check"
-]New value: +[
+  "subject_id",
+  "source_model",
+  "reason",
+  "evidence"
+]
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

Adds valuable behavioral context beyond annotations: two distinct model identities are required, agreement auto-confirms and locks the type, and the subject is not moved. With annotations already indicating idempotency and non-destructiveness, this description meaningfully explains the state-change mechanism.

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?

Two tight sentences, each earning its place: first describes the core operation and threshold, second gives the selection condition. No padding or redundancy.

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?

For a state-changing tool with five parameters and no output schema, it is mostly complete: the required parameters are inferable, the lock/confirm consequence is disclosed, and annotations cover safety. Minor gaps remain around evidence_fingerprint and return/error behavior, but the core invocation path is sufficiently clear.

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 description coverage is only 20%, so the description must carry more weight. It adds meaning to source_model and evidence by describing 'independent AI model' and 'evidence-backed agreement', but it leaves evidence structure, reason purpose, and evidence_fingerprint semantics unexplained.

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?

States a specific action ('submit evidence-backed agreement'), the target ('subject's existing provisional type'), and the meaningful consequence (two agreeing models auto-confirm and lock it). This distinguishes it from siblings like propose_subject_reclassification or get_subject_classification.

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?

Gives an explicit trigger: 'Use this when the current type is already correct and no stricter descendant is justified.' It does not explicitly name the alternative tools or state when not to use it, so it stops short of a 5.

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

A3.8/5.0
Disambiguation4/5

Tools cluster into clear functional families — classification, location, reviews, deliberations, and system — and potentially overlapping operations are explicitly cross-referenced (e.g., enrich_subject vs correct_subject_fact, affirm vs propose reclassification). The main hazard is the resolve_subject / resolve_subject_type / resolve_subject_hierarchy trio, whose near-identical prefixes could mislead an agent at first glance despite well-written descriptions.

Naming Consistency4/5

The surface is dominated by a consistent snake_case verb_noun pattern with stable verb families: get_*, list_*, resolve_*, set_*, save_*, create_*, register_*, and submit_*. Minor deviations — bare-verb fetch and search, and the noun-led vocabulary_index — break the pattern slightly but do not obscure it.

Tool Count3/5

34 tools is heavy and above the preferred range, and the classification family alone accounts for ten tools with substantially duplicated vocabulary guidance. The count is partially earned, however, because the server genuinely spans several subsystems — reviews, subject classification, location assertions, deliberations, and governance — each with its own lifecycle.

Completeness4/5

Each subsystem has thorough lifecycle coverage: deliberations (create/claim/contribute/get/list/resolution), reviews (save/fetch/delete/list/visibility), location (assert/list/resolve), and classification (propose/affirm/reopen/relationships/aliases). Minor gaps include no way to edit review content, no direct list-all-subjects endpoint, and no retirement path for fields or aliases.

Resources