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Save AI-derived assessment

save_assessment
Idempotent

Save separately attributed AI analysis against the exact review it evaluates. When the client supports concurrent tool calls, submit independent writes concurrently in batches of up to 10. Do not batch dependent operations until their prerequisites are confirmed. Reuse the same canonical key for the same subject and derive deterministic idempotency keys from a stable run identifier, target and operation so retries and restarted conversations safely return existing writes instead of creating duplicates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
analysisNo
evidenceNo
conclusionNo
confidenceNo
source_modelNo
experience_idYes
assessment_typeYes
idempotency_keyYes

TDQS

B3.1/5.0
Behavior4/5

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

The description adds meaningful behavioral detail beyond the annotations by explaining how idempotency keys should be derived, why concurrent writes should be batched in groups of up to 10, and that retries should return existing writes. This supplements the idempotentHint annotation with actionable, non-obvious 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 dense but efficient, with a clear front-loaded purpose and two additional sentences that each add non-redundant operational guidance. The phrasing is somewhat complex, but every sentence earns its place.

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

Completeness2/5

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

For a tool with 8 parameters, zero schema description coverage, no output schema, and a large sibling set, the description is incomplete. It covers concurrency and idempotency well, but omits parameter semantics, return behavior, and any guidance distinguishing this tool from alternatives, leaving agents to guess at core invocation details.

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?

Schema description coverage is 0%, so the description is the only place for parameter meaning, but it does not explain most parameters such as experience_id, assessment_type, analysis, evidence, conclusion, confidence, or source_model. It only obliquely references idempotency_key derivation, leaving required and optional parameters largely undefined.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Save'), a specific resource ('separately attributed AI analysis'), and the target ('against the exact review it evaluates'). This makes the tool's core function clear, though it does not explicitly name sibling tools or contrast itself with save_experience or submit_contribution.

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 provides operational guidance about batching concurrent writes and idempotency, but it does not explain when to use this tool versus alternatives. No sibling tool is mentioned, and there is no condition or exclusion that would help an agent decide between save_assessment and similar 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

A3.6/5.0
Disambiguation3/5

Most tools are separated by resource and action, but the classification cluster (affirm_subject_classification, propose_subject_reclassification, reopen_subject_classification, get_subject_classification) and the three resolve_subject* tools require close reading to distinguish. Long descriptions help, but an agent could easily pick the wrong member of those clusters.

Naming Consistency4/5

The dominant pattern is verb_noun in snake_case (create_deliberation, list_reviews_by_visibility, resolve_subject_type, set_review_visibility). Minor deviations such as bare 'fetch' and 'search' and mixed verbs like affirm vs propose vs reopen are readable and do not break the convention.

Tool Count2/5

34 tools substantially exceeds the 25+ threshold even for a server with multiple subdomains. The many classification, type, and location variants add cognitive load, and several could be consolidated or hidden behind a smaller workflow-oriented surface.

Completeness4/5

The set covers the main lifecycles: review save/fetch/assess/delete/visibility, subject resolution/enrichment/correction, classification with voting/reopening, deliberations, locations, and vocabulary management. Minor gaps remain, such as no direct full-subject getter and no update to review text, but these appear intentional in an immutable-review design.