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Glama

Forget

forget
DestructiveIdempotent

Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key to delete

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "key": "user_research_topic"
      +  }
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Consistent with annotations (destructiveHint=true). Adds context about why to delete (stale, done, sensitive data). However, does not mention idempotency (idempotentHint=true) or error behavior if key not found, which leaves 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?

Two concise sentences with no waste. Purpose is front-loaded, guidelines follow immediately. Efficient and clear.

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?

Adequately covers purpose and usage context given no output schema. Lacks mention of what happens if key is missing or that deletion is permanent, but annotations compensate for destructive nature.

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?

Only one parameter with full schema coverage. The description adds no additional meaning beyond the schema's 'Memory key to delete'. Baseline score of 3 is appropriate.

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 verb 'Delete', the resource 'memory', and the method 'by key'. It distinguishes itself from siblings like 'remember' and 'recall' by indicating it is the deletion operation.

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?

Provides specific contexts for use: stale context, task completion, clearing sensitive data. Implicitly indicates when to use but does not explicitly state when not to use, though the pairing with 'remember' and 'recall' suggests alternatives.

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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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also overlap. The server name suggests a German dictionary, but most tools are unrelated, causing confusion about the set's focus.

Naming Consistency2/5

Tool names lack a consistent pattern: some use verb_noun (ask_pipeworx, compare_entities), some noun_noun (ai_visibility_check, dwds_frequency), some single words (forget, lemma), and some are acronyms (kwic). This mixed convention makes prediction of tool names difficult.

Tool Count3/5

At 36 tools, the set is large but not extreme. However, it attempts to cover too many domains (German language, prediction markets, AI visibility, npm packages, etc.), making it feel bloated and unfocused for a server named 'Dwds'.

Completeness2/5

For the German dictionary focus implied by the server name, tools are minimal (dwfs_frequency, lemma, snippet) and two are retired. For the broader data platform, there are gaps like no dedicated SEC filing search tool, relying on generic ask_pipeworx. The set feels incomplete for both intended purposes.