forgetDelete 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.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, so the description's 'Delete' and 'clear sensitive data' are consistent but add minimal extra insight. The description could mention irreversibility but is not necessary given the simple action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences cover purpose, usage, and context without wasted words. The most critical information is front-loaded in the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter, no-output-schema tool, the description fully enables an agent to understand purpose, when to use, and how to use it. Pairing hints with sibling tools round out the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. The description adds no additional semantics beyond restating 'by key' and giving an example, which is adequate but not additive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and the resource ('a previously stored memory by key'). It distinguishes itself from sibling tools 'remember' and 'recall' by indicating it is the deletion counterpart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides three clear usage scenarios: stale context, task completion, and clearing sensitive data. It also mentions pairing with 'remember' and 'recall', indicating when not to use it in isolation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set has significant overlap among query and research tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) and among entity/company tools (entity_profile, compare_entities, recent_changes). Despite detailed descriptions, an agent would struggle to select the correct tool without careful reading, especially for nuanced differences.
Tool naming is inconsistent: some start with verbs (ask_, generate_, validate_, scan_, subscribe) while others are nouns (entity_profile, popular, trending, search, recent_alerts, recent_changes). The snake_case style is consistent, but the verb_noun pattern is not, making predictions of tool names difficult.
38 tools is on the high side for a single server, but the scope is broad (general query, research, Trakt, subscriptions, memory). The count is appropriate for the wide range of functionality, though some tools could be merged to reduce cognitive load.
The tool set covers a very wide range of tasks: querying, research, entity profiles, comparisons, subscriptions, memory, Trakt operations, etc. For the Trakt domain, it has all essential operations (search, get, list, trending). The Pipeworx side has a comprehensive set for data access, grounding, and validation. Minor gaps exist (e.g., no update for subscriptions), but overall it is well-covered.