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Jau-app

JauMemory MCP Server

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by Jau-app

agent_memory

Link memories to agents or recall agent-specific memories, using filters for category, project, and search query.

Instructions

Link memories to agents or recall agent-specific memories.

Usage Examples: // Link a memory to an agent agent_memory({ action: "link", agentId: "frontend-dev", memoryId: "mem-123-456", category: "learning", projectContext: "webapp" })

// Recall all memories for an agent agent_memory({ action: "recall", agentId: "backend-dev" })

// Search agent memories agent_memory({ action: "recall", agentId: "code-reviewer", query: "authentication", category: "error", limit: 10 })

// Project-specific recall agent_memory({ action: "recall", agentId: "test-engineer", projectContext: "api-service", category: "task" })

Memory Categories:

  • task: Assigned tasks and TODOs

  • learning: Things the agent learned

  • error: Errors encountered

  • solution: Solutions found

  • reflection: Agent reflections

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
agentIdYesAgent ID
memoryIdNoMemory ID (for link action)
categoryNoMemory category like task, learning, error
projectContextNoProject name for context
queryNoSearch query (for recall action)
limitNoMax results to return
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It explains the two actions and required parameters, but does not disclose side effects (e.g., whether linking creates a new memory or associates an existing one), authorization needs, or rate limits. Partial transparency.

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 front-loaded with the purpose and includes well-structured examples and a category list. While somewhat lengthy, each section serves a purpose. Could be more concise, but it is structured logically.

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

Completeness4/5

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

Given 7 parameters, no output schema, and no annotations, the description covers the two actions, all parameters through examples, and provides a category list. However, it does not describe the return format of recall, which would be helpful for completeness.

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

Parameters4/5

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 value by defining memory categories and showing parameter combinations in examples, which enhances understanding beyond the schema's property descriptions.

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 'Link memories to agents or recall agent-specific memories' and provides specific usage examples for both actions. It effectively distinguishes the tool's purpose from siblings by focusing on agent-memory linking and recall.

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

Usage Guidelines3/5

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

Examples show when to use link vs recall, but there is no explicit guidance on when to use this tool instead of siblings like 'remember' or 'memorize'. The description provides clear context but lacks exclusions or alternative tool comparisons.

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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