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Jau-app

JauMemory MCP Server

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by Jau-app

agent_reflection

Create and retrieve agent reflections to capture lessons learned from mistakes, successes, and collaborations, enabling continuous improvement for AI agents.

Instructions

Create and retrieve agent reflections for continuous improvement.

Usage Examples: // Create a learning reflection agent_reflection({ action: "create", agentId: "frontend-dev", reflectionType: "learning", content: "Discovered that React.memo can prevent unnecessary re-renders in large lists", lessonsLearned: [ "Use React.memo for expensive components", "Profile before optimizing", "Not all components need memoization" ] })

// Create a mistake reflection agent_reflection({ action: "create", agentId: "backend-dev", reflectionType: "mistake", content: "Forgot to add database indexes, causing slow queries in production", lessonsLearned: [ "Always analyze query patterns before deployment", "Add indexes for frequently filtered columns", "Monitor query performance in staging" ] })

// Create a collaboration reflection agent_reflection({ action: "create", agentId: "code-reviewer", reflectionType: "collaboration", content: "Worked with frontend-dev to establish better PR review guidelines", lessonsLearned: [ "Clear PR descriptions save review time", "Automated checks reduce manual review burden" ], relatedAgents: ["frontend-dev", "test-engineer"] })

// List all reflections for an agent agent_reflection({ action: "list", agentId: "test-engineer" })

// List specific type of reflections agent_reflection({ action: "list", agentId: "project-manager", reflectionType: "success" })

Reflection Types:

  • learning: New knowledge or insights gained

  • mistake: Errors made and lessons learned

  • success: Achievements and what worked well

  • collaboration: Insights from working with other agents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
agentIdYesAgent ID
reflectionTypeNoType of reflection
contentNoReflection content (for create)
lessonsLearnedNoKey takeaways
relatedAgentsNoOther agents involved
Behavior4/5

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

No annotations provided, so description carries the burden. It discloses that the tool supports two actions (create and list) and defines reflection types. It does not mention persistence, side effects, or permissions, but the examples imply typical behavior.

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?

Description is well-structured with clear sections for usage examples and reflection type definitions. However, it is verbose due to multiple examples; could be slightly more concise while retaining clarity.

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?

For a tool with 6 parameters and no output schema, the description covers all aspects: purpose, actions, parameters, reflection types, and typical usage patterns. It is complete and actionable.

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 coverage is 100%, but description adds significant value beyond schema: it provides typical values for lessonsLearned and relatedAgents, and explains reflection types with context. The examples clarify parameter usage effectively.

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: 'Create and retrieve agent reflections for continuous improvement.' It distinguishes from sibling tools like agent_memory and agent_collaboration by focusing on reflections with specific types (learning, mistake, etc.).

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?

Usage examples show when to use create vs list and different reflection types. However, it does not explicitly exclude alternatives or mention when not to use this tool over siblings like agent_error_learning.

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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