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 provide destructiveHint=true and idempotentHint=true, so the description's 'Delete' adds little beyond that. It does not elaborate on permanence or side effects, leaving the annotations to carry the behavioral weight.
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?
Two sentences, no fluff, front-loaded with the action and purpose. Every sentence contributes meaning.
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 tool with one parameter, high schema coverage, and clear annotations, the description is sufficiently complete. It states purpose, usage context, and sibling relationships. No output schema, so return value explanation is not needed.
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 description for the 'key' parameter. The description adds only 'by key', which is redundant. It does not clarify format or constraints beyond what the schema provides. Baseline score of 3 is appropriate.
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 states 'Delete a previously stored memory by key', which clearly identifies the verb and resource. It mentions pairing with 'remember' and 'recall' but does not explicitly distinguish when to use this over those siblings. The purpose is clear but lacks direct sibling differentiation.
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 describes when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with related tools, providing implicit context for alternatives. No explicit 'when not to use' statement, but the given guidance is specific and actionable.
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 scopes, and the long cross-referencing descriptions help a lot. However, ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx by the server's own description, and the polymarket_* family plus ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping boundaries that could mislead an agent.
The vast majority follow a clear verb_noun snake_case pattern like get_odds, list_sports, resolve_entity, and subscribe. A few exceptions such as odds_api_quota, pipeworx_feedback, polymarket_arbitrage, and recall break the pattern slightly, but the overall convention is predictable.
37 tools is well past the 25+ threshold and feels bloated for a server named 'Odds Api'. Many tools are meta-platform utilities — memory, feedback, trending, dependency scanning, llms.txt generation — that have no obvious connection to an odds API and make the surface hard to navigate.
The odds domain is well covered: list_sports, get_events, get_odds, get_event_odds, get_scores, quota tracking, and subscriptions form a coherent read/monitor workflow. Minor gaps exist — no historical odds or a single-event detail endpoint — but agents can complete core odds research and monitoring tasks without dead ends.