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richardoros

threadline-core

by richardoros

log_agent_event

Report work events to record decisions, blockers, and open loops for session continuity.

Instructions

Report a work event to Threadline — call this as you work.

Parse and ingest a single AgentEventIn JSON payload. Returns a summary of what was derived (decision count, open-loop count).

WHEN TO CALL

  • At significant checkpoints (code written, tests passing, a decision made).

  • When you encounter a blocker.

  • When you notice something deferred (open loop).

EVENT SHAPE (compact example)

{
  "event_type": "checkpoint",
  "project_key": "my-project",
  "agent": {"name": "claude_code"},
  "session_id": "<from start_session>",
  "summary": "Implemented login route; all tests pass.",
  "details": {
    "decisions": ["Use JWT over session cookies for stateless auth"],
    "open_loops": ["Rate limiting not yet implemented"],
    "files_changed": ["src/auth.py", "tests/test_auth.py"],
    "verification": ["uv run pytest -q"]
  }
}

Returns

dict with keys: event_id, session_id, decisions_created, open_loops_created. On parse/validation error: {"error": true, "message": "..."} — fix and retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the return values (including error case) and that the tool ingests and derives summary counts. While it doesn't detail side effects or authorization, it is sufficient for a logging tool.

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 well-structured with headings, bullet points, and a code block. It is appropriately sized for the tool's complexity, though slightly lengthy. Every part adds value.

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 has one parameter, an output schema (implied but not provided), and no nested objects, the description is complete. It covers purpose, usage, parameter format, and return values. No gaps remain.

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?

The input schema has 0% description coverage, but the description compensates fully with a detailed example JSON and explanation of the expected shape. It also describes the return values, adding significant value 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 the tool's purpose: 'Report a work event to Threadline' and 'Parse and ingest a single AgentEventIn JSON payload.' It uses a specific verb ('report') and resource ('work event'), and the purpose is distinct from sibling tools like 'start_session' or 'get_decisions'.

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?

A 'WHEN TO CALL' section provides explicit guidelines: at significant checkpoints, blockers, or deferrals. This helps the agent decide when to use this tool, though it does not explicitly mention when not to call or alternatives.

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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