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. The description adds context about clearing sensitive data and states the action is deletion, but does not explain behavior when key does not exist or any side effects. The additional value is minimal.
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 sentences) with no wasted words. The first sentence states the core purpose, and the second provides usage guidance. 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 simple tool with one parameter, no output schema, and clear annotations, the description is complete. It covers purpose, usage scenarios, and sibling relationships, leaving no significant gaps.
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%, so baseline 3. The description mentions 'by key' which matches the schema's parameter. No additional semantics or format details are provided beyond the schema.
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 and resource: 'Delete a previously stored memory by key'. It clearly distinguishes from sibling tools 'remember' and 'recall' by explicitly pairing with them, implying they serve different purposes.
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 guidance on when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also mentions pairing with 'remember' and 'recall' as alternatives.
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.
Several natural-language query tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying Pipeworx catalog. The descriptions do clarify the differences eventually, but the boundaries are subtle enough that an agent could easily pick the wrong one, and discover_tools/suggest_questions also serve a similar onboarding role.
Almost all names are snake_case and readable, but they mix verb-first names (ask_, compare_, discover_, validate_) with noun-first names (entity_profile, recent_changes, polymarket_arbitrage, pipeworx_trending). The domain prefixes like hilma_, polymarket_, and pipeworx_ help navigation, but there is no single predictable verb_noun convention across the set.
At 34 tools, the surface is too large for the amount of genuine functional diversity. Several tools are near-duplicates (ask_pipeworx_beta vs ask_pipeworx, ai_visibility_check vs scan_competitor_ai_presence, suggest_questions vs discover_tools), and the set would be noticeably tighter around 20-25 tools without losing coverage.
The major workflow clusters are well covered: research/query modes, entity resolution and comparison, prediction-market analysis, memory, and subscription lifecycle management all have their key operations present. Minor gaps exist — such as Hilma notices returning only index metadata rather than full notice text — but there are no critical dead ends for the server's apparent multi-domain research purpose.