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 declare destructiveHint=true and readOnlyHint=false, so the agent knows it is a destructive write operation. The description adds valuable context about what kind of data to clear (sensitive data, stale context) and that it removes saved memories. This goes beyond the structured annotation hints without contradicting them.
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?
The description is two sentences, front-loaded with the primary action, followed by usage guidance and related tools. Every word earns its place with no filler or repetition.
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 one-parameter tool with a complete schema and annotations, the description covers what the tool does, when to use it, and how it relates to siblings. No output schema is present, but a simple deletion tool does not require return-value details. The description is fully adequate for tool selection and invocation.
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 the 'key' property described as 'Memory key to delete'. The description's phrase 'by key' adds little beyond the schema. Baseline score of 3 is appropriate because the schema fully documents the parameter, and the description does not need to compensate.
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 uses a specific verb ('Delete') and resource ('previously stored memory by key'), clearly distinguishing it from sibling tools like 'remember' and 'recall'. It exactly states the core function without ambiguity.
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?
The description provides explicit use cases: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with 'remember and recall', giving relational context. However, it does not explicitly state when NOT to use the tool, which would make it a 5.
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.
Most tools have distinct purposes, but there is overlap between query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, and between prediction market tools like bet_research and polymarket_edges. Descriptions help differentiate, but the boundaries are not always clear.
Tool names are all snake_case but lack a consistent pattern. Some start with verbs (ask, compare, find), others are nouns (autocomplete, entity_profile), and many are long phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). The naming feels ad-hoc and not easy to predict.
At 35 tools, this server is over-packed for a server named 'words'. Many tools are unrelated to words (e.g., prediction markets, subscriptions, entity profiles). The scope is too broad, making it feel like a catch-all rather than a coherent set.
The word tools are limited (only 6), leaving obvious gaps for a word-focused server (e.g., no dictionary lookup, no word definitions). However, the server covers a wide range of data domains through meta-tools like ask_pipeworx, which compensates but makes the purpose unclear.