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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "key": "user_research_topic"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description's 'Delete' aligns with the annotations' destructiveHint=true and readOnlyHint=false. It adds context about clearing sensitive data, but the annotations already cover the destructive nature. No additional behavioral traits (e.g., side effects, required permissions) are disclosed beyond the annotations.
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 extremely concise: two short sentences. The first sentence defines the action, and the second provides usage guidance. There is no redundant or irrelevant information; every sentence earns its place.
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 tool with one required parameter and no output schema, the description sufficiently covers the tool's purpose and when to use it. It does not need to explain return values, and the usage scenarios are adequate for an agent to decide when to invoke it.
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?
The schema has 100% coverage with one parameter 'key' described as 'Memory key to delete'. The description does not add any further meaning or usage details beyond the schema, so the 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 clearly states the verb 'Delete', the resource 'previously stored memory', and the method 'by key'. It also distinguishes from sibling tools 'remember' and 'recall' by naming them, making it easy for an agent to understand its purpose.
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 explicitly provides three scenarios for using the tool: when context is stale, task is done, or to clear sensitive data. It also suggests pairing with 'remember' and 'recall'. While it does not specify when not to use, the guidance is clear 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 detailed 'use when' guidance and the polymarket/entity clusters are distinguishable, but ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research sit close together. Many other tools (ai_visibility_check vs scan_competitor_ai_presence, bet_research vs polymarket_edges) require careful reading to keep separate.
Consistent snake_case and recognizable subfamilies (ask_pipeworx*, polymarket_*, get_*) keep names readable. However the overall set mixes verb_noun (get_schedule, resolve_entity), bare verbs (remember, forget), noun phrases (recent_alerts, pipeworx_trending), and compound noun names (bet_research, polymarket_fill_risk), so there is no single predictable convention.
35 tools is well over the 25+ threshold and spans many unrelated concerns: F1 data, universal data lookup, prediction markets, subscriptions, memory, npm scanning, and AI visibility. While a broad data platform can justify a large surface, the mix of one-off and meta tools makes this feel bloated rather than well-scoped.
For a server named F1 the surface is thin: it covers schedule, driver profiles, race results, and driver standings but lacks constructor standings, qualifying, team/circuit data, and a driver list. The surrounding Pipeworx tools provide depth in other domains, but they do not fill the F1-specific gaps.