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Item change history

layerz_history_for_item
Read-only

Trace the per-item change history for one UID — when it was added, modified, or removed, who did it, and which fields changed each time. Walks the version history oldest→newest, diffs consecutive snapshots, and reports only events where the requested UID changed. fields is the same shape as layerz_diff field-level entries. Returns { uid, events: [{ revision_id, version_number, created_at, actor_user_id, trigger_type, status: "added"|"removed"|"modified", fields?: [{ field, oldValue, newValue }] }] } — newest first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uidYesItem UID (8-char or role-prefixed form like `d:abc12345`).
limitNoMax number of versions to scan (default 50, clamped to plan retention).
model_idNoTarget model UUID. Required for user-scoped API keys; ignored (or validated against scope) for model-scoped keys.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

The description goes beyond the readOnlyHint annotation by explaining the traversal method (oldest→newest, consecutive diffing) and the output structure, which is not present in annotations. It clarifies that only events where the UID changed are reported, adding behavioral depth that aids the agent in understanding what to expect.

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 compact, using three sentences to convey the core action, method, and output format. Key information like the return structure is front-loaded, and the reference to layerz_diff for field semantics avoids redundancy. It's slightly dense but well-organized.

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 read-only tool with no output schema, the description provides a thorough return structure and explains the scanning behavior (limit, retention) implicitly through the schema. It covers the key aspects an agent needs, though it could explicitly mention the limit default and max, but these are already in the schema, so completeness is adequate.

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?

Since schema description coverage is 100%, the baseline is 3. The description reinforces the `fields` parameter's shape (same as layerz_diff field-level entries) and details the return format, but doesn't add substantial new meaning for uid, limit, or model_id beyond their schema descriptions, which are already explicit.

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 it traces per-item change history for a UID, specifying the operations (added, modified, removed), actor, and field changes. It differentiates itself from sibling layerz_history by focusing on a single item, making its purpose unambiguous and distinct.

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?

The description implicitly conveys usage by emphasizing per-item tracing and returns events for a requested UID, contrasting with layerz_history which likely covers full model history. It doesn't explicitly state 'when not to use' but provides sufficient context for an agent to select this tool for item-level audits.

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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