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ledger_log_action

Log a single autonomous action taken during a self-directed dev loop, tagging it with an impact tier, category, and description to maintain an audit ledger.

Instructions

Record a single autonomous action taken during a self-directed dev loop.

Use this to log any action the agent takes on its own initiative, tagged with a tier indicating how consequential it was.

Args: tier (int): Impact tier. Must be 0 (routine/info), 1 (notable), or 2 (high-impact, e.g. irreversible or user-facing). category (str): Short category label, e.g. "refactor", "deploy", "file_write", "dependency_change". Must be non-empty. description (str): Human-readable description of what was done. Must be non-empty. project (Optional[str]): Project/repo name this action belongs to.

Returns: dict: {"id": int} — the new action's row id.

Errors: Raises ValueError if tier is not in {0,1,2} or category/description are empty, with a message explaining the valid values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierYes
projectNo
categoryYes
descriptionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description discloses return value format (id int), error behavior (ValueError for invalid tier and empty strings), and tier semantics. These details go beyond what annotations provide, offering actionable behavioral context.

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?

The description is front-loaded with a one-sentence summary, then organized into Args, Returns, and Errors sections. It is concise, well-structured, and every sentence adds useful information.

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 the tool's modest complexity, the presence of an output schema, and annotations, the description fully covers purpose, parameters, return value, and error cases. It is complete enough for safe and correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema only provides types, while description adds allowed tier values (0,1,2), example categories, non-empty constraints, and optionality of project. This significantly enriches parameter understanding, especially with 0% schema coverage.

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 records a single autonomous action in a dev loop, with a specific verb and object. It distinguishes from sibling tools by specifying 'autonomous action' rather than dispatch or verdict, giving it a distinct purpose.

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?

It provides clear context for when to use: logging any action the agent takes on its own initiative, tagged with a tier. It does not explicitly exclude other ledger tools, but the context is sufficient for an agent to choose this among siblings.

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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