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jarvis-orb

jarvis-orb

by TheStack-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_save

Save memories categorized as episodic, semantic, project, or procedural. Store content with optional tags, source, and project to build persistent AI memory.

Instructions

Save a memory to the brain. Types: episodic, semantic, project, procedural.

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": {
      "content": "<content>"
    },
    "name": "memory_save"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNo
sourceNo
contentYes
projectNo
memory_typeNoepisodic

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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 only says 'Save a memory' without disclosing side effects, idempotency, or authentication requirements. Listing types adds minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one sentence plus a list), but it could be more structured. It front-loads the main action, but lacks organization for the types list.

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

Completeness2/5

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

Given 5 parameters (1 required) and no annotations, the description is too sparse. It does not explain memory types, their usage, or the return value (despite an output schema). Key contextual information is missing.

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 should compensate. It only hints at the 'memory_type' parameter by listing types, but ignores 'tags', 'source', 'project', and 'content'. No parameter explanations are added beyond the raw schema.

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?

Description clearly states the action 'Save a memory' and specifies the resource ('to the brain'). It lists four memory types, making the purpose specific and distinct from sibling tools like memory_search or memory_verify.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as entity_create or memory_search. No mention of prerequisites or conditions.

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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