marsnme

explain_memory

Trace the provenance of a long-term memory chunk to understand its origin, creation, and source material.

Instructions

Trace the full provenance of a long-term memory chunk in demo.marsvault_chunks — where it came from, how it was created, and what source material it was derived from. Returns the original source memory, session, tool, and timeline information. Use this to understand why a piece of knowledge exists or to verify its reliability.

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": {
      "id": "<id>"
    },
    "name": "explain_memory"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes	UUID of the long-term memory chunk to explain

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.1.1Jun 10, 2026

changedInput schema / properties / id / description

Previous value: -"Long-memory chunk id (UUID)"New value: +"UUID of the long-term memory chunk to explain"

First observedv0.1.0Jun 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explains that the tool returns provenance information (original source memory, session, tool, timeline) and implies a read-only operation. No contradictions.

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

Conciseness5/5

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

The description is two sentences long, front-loads the main action, and every sentence provides value without redundancy. It is concise and structured effectively.

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?

Given the tool has one parameter and no output schema, the description fully covers its purpose, usage, and return values. It is complete for an AI agent to understand selection and invocation.

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 description coverage is 100% for the single parameter 'id', which is described as a UUID. The description elaborates on how this parameter relates to the tool's purpose, adding context beyond the 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?

The description uses a specific verb 'trace' and clearly identifies the resource 'long-term memory chunk' and its provenance. It distinguishes from sibling tools which handle creation, deletion, or recall.

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?

The description explicitly states when to use the tool: 'to understand why a piece of knowledge exists or to verify its reliability.' It does not provide explicit exclusions or alternatives, but the context of siblings implies when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server