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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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate destructiveHint=true. The description adds behavioral context: clearing sensitive data and using when stale/done. 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?

Two sentences, front-loaded with purpose, no wasted words. Highly concise.

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?

Simple tool with one required parameter. Covers purpose, usage context, and relationship to other tools. No output schema needed.

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 100% and describes the 'key' parameter as 'Memory key to delete'. The description does not add additional parameter semantics beyond this.

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 deletes a memory by key. It uses a specific verb ('delete') and resource ('memory'), and distinguishes 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 Guidelines5/5

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

Explicitly states when to use: when context is stale, task done, or to clear sensitive data. Also mentions pairing with 'remember' and 'recall', providing clear guidance on 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

A3.9/5.0
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and composite tools like entity_profile, recent_changes, compare_entities, and validate_claim draw on similar company-data sources. However, the descriptions are extremely detailed with explicit usage guidance, so an agent can usually pick correctly despite the overlap.

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, list_subscriptions, validate_claim), and domain prefixes like nola_, pipeworx_, and polymarket_ help group tools. Minor deviations such as entity_profile, recent_changes, and polymarket_edges are still readable and do not undermine the overall pattern.

Tool Count2/5

At 34 tools, the set spans far beyond the 'Data Nola' name: New Orleans open data, general Pipeworx data research, Polymarket arbitrage, npm dependency scanning, AI visibility, memory, and subscriptions. This breadth makes the surface heavy and forces agents to triage a large and heterogeneous toolset.

Completeness4/5

The broader research platform is well covered: question answering, entity resolution, profiles, comparisons, claim validation, subscriptions, memory, and NOLA dataset querying all have solid lifecycle support. Minor gaps exist—no NOLA dataset metadata or write tools, no Polymarket order execution—but these are acceptable for a read-only data server.