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morpheus_reflect

Record gate outcomes to analyze effectiveness in AI development workflows. Builds a dataset showing which gates catch real issues versus unnecessary ceremony.

Instructions

Record whether a gate caught a real issue or was ceremony.

Call this after each gate fires to build the Reflect dataset. Over time, this data reveals which gates produce value and which burn tokens without changing behavior.

Args: plan_id: The plan ID task_id: The task ID gate: Gate name (e.g., "sibling_read", "fdmc_review", "seraph_assess", "knowledge_gate") caught_issue: True if the gate found something actionable changed_code: True if code was modified because of this gate detail: Brief description of what happened (e.g., "matched singleton pattern from sibling" or "no issues found")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
plan_idYes
task_idYes
gateYes
caught_issueNo
changed_codeNo
detailNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the long-term behavioral purpose ('reveals which gates produce value and which burn tokens'), implying data persistence and analysis. However, it omits idempotency guarantees, side effects, or what the output schema contains despite having one.

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?

Well-structured with purpose front-loaded, followed by timing, value proposition, then parameter documentation. The Args block is lengthy but necessary given zero schema coverage. No wasted words, though the inline parameter documentation could be considered redundant with proper schema descriptions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a 6-parameter logging tool: documents all inputs with examples. However, given no annotations and an existing output schema, the description should ideally mention error conditions (e.g., invalid plan_id) or prerequisites. It leaves operational behavior (idempotency, overwrites) undocumented.

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?

With 0% schema description coverage, the description fully compensates by documenting all 6 parameters in the Args block, including specific semantics ('True if the gate found something actionable') and concrete examples for 'gate' and 'detail' parameters. This is exemplary compensation for schema deficiencies.

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 'Record[s] whether a gate caught a real issue or was ceremony' — specific verb, resource (gate reflection), and scope. It distinguishes itself from sibling tools by focusing on dataset building for value analysis rather than gate execution or status checking.

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?

Explicitly states when to use: 'Call this after each gate fires to build the Reflect dataset.' Provides clear temporal context. Lacks explicit 'when not to use' or named alternatives, though the dataset-building purpose implies this is specifically for logging reflection events.

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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