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Glama

forget

Delete specific data items, entire datasets, or all stored memory across relational, graph, and vector databases.

Instructions

Delete data from memory.

Can target a single data item, a specific dataset (by name or id), or everything the user owns. Removes data from the relational DB, graph DB, and vector DB.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_idNoUUID of a single data item to delete. Must be paired with `dataset` or `dataset_id` so the owning dataset is unambiguous.
datasetNoDataset name to delete entirely.
dataset_idNoUUID of the dataset to delete entirely, or to scope `data_id`.
everythingNoIf true, delete ALL data across all datasets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv1.6.0
    • addedInput schema / properties / data_id / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • removedInput schema / properties / data_id / type
      Removed value: -"string"
    • addedInput schema / properties / dataset / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • removedInput schema / properties / dataset / type
      Removed value: -"string"
    • addedInput schema / properties / dataset_id / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • removedInput schema / properties / dataset_id / type
      Removed value: -"string"
  2. Changed2 schema fields changedv1.5.1
    • addedInput schema / properties / data_id
      Added value: +{
      +  "default": null,
      +  "description": "UUID of a single data item to delete. Must be paired with `dataset`\nor `dataset_id` so the owning dataset is unambiguous.",
      +  "type": "string"
      +}
    • addedInput schema / properties / dataset_id
      Added value: +{
      +  "default": null,
      +  "description": "UUID of the dataset to delete entirely, or to scope `data_id`.",
      +  "type": "string"
      +}
  3. Changed7 schema fields changedv1.5.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / dataset / description
      Added value: +"Dataset name to delete entirely."
    • removedInput schema / properties / dataset / title
      Removed value: -"Dataset"
    • addedInput schema / properties / everything / description
      Added value: +"If true, delete ALL data across all datasets."
    • removedInput schema / properties / everything / title
      Removed value: -"Everything"
    • removedInput schema / title
      Removed value: -"forgetArguments"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "result": {
      +      "items": {},
      +      "type": "array"
      +    }
      +  },
      +  "required": [
      +    "result"
      +  ],
      +  "type": "object",
      +  "x-fastmcp-wrap-result": true
      +}
  4. First observedv1.0.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that records are removed from three types of databases, which is useful. However, it does not mention irreversibility, permission requirements, or potential cascading effects. This is adequate but not comprehensive.

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 succinct, leading with the primary action, then the targeting options, and finally the storage impact. No unnecessary words; every sentence adds value.

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 delete operation, it covers the key aspects: what can be deleted, how scope is specified, and which storage layers are affected. It does not explain error scenarios or side effects beyond deletion, but that is not essential given the presence of an output schema.

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?

The schema already documents each parameter at 100% coverage. The description adds overarching semantics: it clarifies that data_id must be paired with a dataset identifier, and distinguishes three targeting modes. This guidance helps the agent correctly combine parameters beyond what individual descriptions provide.

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 begins with 'Delete data from memory', a precise verb+resource pair, and enumerates the three deletion scopes (single item, dataset by name/id, everything). It also specifies that deletion spans relational, graph, and vector databases, clearly distinguishing it from siblings like remember 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?

The description implies this tool is for deletion but does not explicitly name alternatives or conditions. It could have said 'Use this instead of remember/recall for removing data' to guide the agent, but the intent is clear from context and sibling names.

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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