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log_decision

Record architectural decisions with rationale and alternatives to create a permanent record for future sessions, ensuring context retention.

Instructions

Log EVERY significant architectural decision you make during this session. This creates a permanent record that future sessions will read. If you chose between two approaches, log why. If you changed an existing pattern, explain the reasoning. Future sessions depend on this.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feature_slugYesThe slug of the feature this decision relates to. In repo-local multi-repo projects this may be repo-prefixed (for example "web--authentication").
titleYesA short title for the decision (e.g. "Use Redis for session cache")
descriptionYesFull description including rationale, alternatives considered, and why this was chosen
Behavior4/5

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

No annotations provided, but the description discloses that the tool creates a permanent record that future sessions will read. It implies non-destructive, persistent logging behavior, which is sufficient.

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?

Concise, three sentences with no fluff. Front-loaded with the core purpose, making it easy for an AI agent to quickly grasp the tool's function.

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 simplicity (3 required params, no output schema), the description comprehensively covers purpose, usage, and behavioral context. No gaps.

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?

Schema coverage is 100%, so baseline is 3. The description adds general guidance but does not elaborate on parameter specifics beyond what the schema already provides.

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: to log significant architectural decisions during a session for future reference. It distinguishes itself from siblings like get_decision_log (for reading) and add_session_summary (different scope).

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?

Provides explicit guidance on when to use: after making a decision, especially when choosing between approaches or changing patterns. Could be improved by specifying when not to use, but the context is clear.

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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