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by ricoaiproject-cmd
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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correct

Fix inaccurate memories by superseding the old entry and recording the reason, preventing repeated errors.

Instructions

Use this instead of forget when a memory turns out to be wrong.

Demotes the old memory to superseded (corrected) and creates a new memory that records the reason for the correction. Explicitly recording the mistake prevents the same error from being repeated (a hypercorrection effect).

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>",
      "corrected_content": "<corrected_content>",
      "reason": "<reason>"
    },
    "name": "correct"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYes
reasonYes
sourceNounknown
corrected_contentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
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 It discloses the key behavioral effects: demoting the old memory to superseded and creating a new memory with the reason for correction. It also explains the hypercorrection effect, which adds useful context. However, it does not mention whether the old memory becomes unavailable or any potential side effects, leaving some minor gaps.

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 extremely concise and front-loaded, starting with a clear directive and then explaining the behavioral rationale. Every sentence adds value, with no redundant information.

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 it has no output schema or annotations, the description adequately covers the core workflow: when to use, what happens to the old and new memory, and why this is beneficial. It could be improved by mentioning how to obtain the 'id' or what 'source' is for, but overall it is complete enough for an agent to select and invoke the tool correctly.

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 description coverage is 0%, so the description must compensate. It indirectly explains 'corrected_content' and 'reason' through the narrative, and 'id' is inferable as the memory to correct. However, 'source' is never mentioned, and no explicit parameter mapping is given. This provides moderate help beyond the bare 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 clearly states the tool's purpose: to correct a memory that turns out to be wrong, demoting the old memory and creating a new one with the correction. It uses a specific verb ('correct') and contrasts with the sibling tool 'forget', making its unique role unmistakable.

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

Usage Guidelines5/5

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

Explicitly tells when to use this tool: 'Use this instead of forget when a memory turns out to be wrong.' This names the alternative (forget) and provides a clear decision rule, giving the agent unambiguous guidance.

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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