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Component Changelog Trail

component_changelog_trail

Records an append-only per-component revision history as a JSONL trail next to the component file. Append new entries, read the history, or export a markdown changelog — all offline.

Instructions

Maintains an append-only per-component revision history as a JSONL trail (<component>.trail.jsonl) inside the Obsidian vault, next to the .tox and its provenance sidecar. Three actions: append a new revision entry (with optional sha256 of the .tox, changed-param list, author, and timestamp); read all entries back as JSON; export the trail as a human-readable markdown changelog note rendered into the vault. Offline — no TD bridge required. Pairs with save_component_to_vault and provenance_stamp.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entryNoRequired when action='append'. Ignored for read/export.
actionNoappend: add a new revision entry. read: return all entries as JSON. export: render the trail as a markdown changelog note next to the .tox.read
includeShaNoOn append, hash the .tox bytes with sha256 and store it on the entry — lets you cross-reference with provenance_stamp's sidecar.
componentPathYesVault-relative path to the .tox file (e.g. 'Components/MyFx.tox'). The trail is stored as a sibling file '<componentPath>.trail.jsonl'.
exportNoteNameNoOn export, the markdown filename (defaults to '<component>.CHANGELOG.md' next to the .tox).
Behavior5/5

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

The description details the append-only nature, file format (JSONL), storage location, and offline capability, going well beyond the sparse annotations. It also explains the behavior of each action, providing comprehensive behavioral transparency.

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 front-loaded with the main purpose and is concise, though slightly dense. It effectively communicates key information without unnecessary words.

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 no output schema, the description explains return formats (JSON for read, markdown for export) and the trail file format. It covers all three actions and their parameters adequately for a tool of this complexity.

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?

With 100% schema description coverage, the description adds some value by explaining defaults and context (e.g., 'Ignored for read/export'), but does not significantly enhance understanding 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 it maintains an append-only per-component revision history as a JSONL trail, listing three distinct actions (append, read, export). It distinguishes from siblings by mentioning it pairs with save_component_to_vault and provenance_stamp.

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 provides context for when to use the tool (offline, no TD bridge required) and mentions related tools. However, it does not explicitly state when not to use it or provide direct comparisons with alternatives, which slightly limits guidance.

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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