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memorygraph

memorygraph

by xbrxr03
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Forget observation

forget
DestructiveIdempotent

Delete a single source observation and propagate direct retractions to keep your memory graph accurate and private.

Instructions

Privacy-delete one source observation and propagate direct retractions.

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": {
      "bank": "<bank>",
      "observation_id": "<observation_id>"
    },
    "name": "forget"
  }
}

Input Schema

NameRequiredDescriptionDefault
bankYesBank slug or UUID.
workspaceNoOptional workspace label stored on observations.
observation_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=true, and the description adds value by explaining the 'privacy-delete' nature (implying data sanitization) and the propagation of retractions. This goes beyond the bare annotation flags and gives a clearer picture of side effects, though it doesn't detail the propagation mechanism itself.

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 a single, compact sentence that front-loads the core action ('Privacy-delete') and immediately follows with the key side-effect. There is zero fluff; every word carries functional meaning, making it an exemplary concise definition.

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?

For a simple deletion tool with 3 parameters and no output schema, the description covers the essential action and side effects. It does not mention return values, error handling, or prerequisites, but given the low complexity and annotations covering idempotency and destructiveness, it is adequately complete.

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

Parameters3/5

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

Schema coverage is 67%, meaning two of three parameters have descriptions in the schema. The tool description does not add any semantic detail about parameters (e.g., what 'bank' or 'observation_id' specifically mean in this context). Since coverage is not high (>80%) but not low (<50%), the baseline 3 applies, and the description makes no attempt to compensate.

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 states a specific verb ('Privacy-delete') and resource ('one source observation') and adds a distinguishing side-effect ('propagate direct retractions'). This clearly sets it apart from siblings like 'record' or 'explain', making the tool's purpose instantly recognizable.

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 is given for when to use this tool versus alternatives. It does not mention conditions, exclusions, or scenarios where a different sibling (e.g., 'correct') would be more appropriate. The usage context is entirely left to the agent to infer.

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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