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.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=true, which covers the safety profile. The description adds context about deleting agent-saved memories and clearing sensitive data, but does not disclose additional side effects beyond the annotated destructive behavior.
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 concise sentences, each serving a purpose: definition, usage scenarios, and related tool pairing. No redundant or filler content.
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 required parameter and no output schema, the description covers what, when, and related tools. The idempotent and destructive annotations complete the behavioral context, making the tool fully understood.
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 input schema fully documents the single 'key' parameter with a clear description, achieving 100% coverage. The tool description reinforces 'by key' but adds no additional parameter semantics beyond what the schema already provides.
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?
Description uses the specific verb 'Delete' and identifies the resource as 'a previously stored memory by key', clearly distinguishing it from siblings like remember and recall. It precisely states the tool's function.
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?
Provides explicit when-to-use scenarios: stale context, task completion, or clearing sensitive data. It also mentions pairing with remember and recall, offering related tool context, though it lacks formal exclusions.
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 clearly distinct purposes, but there are overlapping clusters: the three ask_pipeworx variants and multiple polymarket analysis tools can cause selection ambiguity. Descriptions help, yet boundaries between entity_profile, compare_entities, recent_changes, and ask_pipeworx require careful reading.
Tool names almost all follow snake_case with verb-noun or verb-phrase structure (ask_pipeworx, validate_isin, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are consistent. Minor deviations include one-word verbs (remember, recall, forget) and adjective-noun names (recent_alerts, recent_changes, entity_profile).
With 34 tools, the surface is well above the 25-tool threshold that typically feels heavy, even though the Pipeworx platform is broad in scope. The server named 'Isin' exposes a large toolkit far beyond its apparent identifier-focused purpose, making the count feel excessive.
The broader data-access, research, subscription, and utility workflows are well covered, including discovery, grounded queries, entity profiles, comparisons, and claim validation. Minor gaps include the lack of direct pipeworx:// URI reading and ISIN issuer resolution, but agents can work around these.