DecisionsSearch

memory.reflect

Reflect on completed tasks to distill learned insights and persist them as durable knowledge. Use after finishing work to store decisions, patterns, and outcomes for future recall.

Instructions

Extrai conhecimento de uma task CONCLUÍDA e persiste (loop de pós-task).

Use when: terminou um trabalho e quer destilar o que foi aprendido em memória durável. Do NOT use for: RECUPERAR conhecimento (memory.query); pré-task (memory.context). outcome: 'completed' | 'failed' | 'partial'. Example: memory.reflect(task_description='Implementei HyDE opt-in', changes='...', project='DecisionsSearch').

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "task_description": "<task_description>",
      "changes": "<changes>"
    },
    "name": "memory.reflect"
  }
}

Input Schema

Name	Required	Default
`changes`	Yes
`outcome`	No	completed
`project`	No
`task_description`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 18, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It mentions 'loop de pós-task' and persistence but does not detail what the loop does, whether the operation is destructive or reversible, or what exact side effects occur. The behavioral picture is partial.

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 concise with two sentences plus usage guidelines and an example. It is well-structured: purpose, usage, do-not-use, allowed outcomes, example. Minor reduction for being slightly verbose in the example, but overall efficient.

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?

Given 4 parameters, no output schema, and no annotations, the description is incomplete. It covers the high-level purpose and usage but lacks details on parameter semantics, return value, and behavioral nuances like the 'loop de pós-task'. It is adequate but has clear gaps.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It only mentions the parameters in an example and lists outcome values, but does not explain the meaning of 'changes', 'task_description', or how 'project' is used. This leaves significant gaps in understanding the parameter semantics.

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 extracts knowledge from a completed task and persists it. It explicitly distinguishes from sibling tools like memory.query (retrieve) and memory.context (pre-task), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use ('terminou um trabalho') and when-not-to-use ('RECUPERAR conhecimento', 'pré-task'). Also lists the outcome enum values and includes a concrete example, giving the agent clear guidance on proper invocation.

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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