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 mark destructiveHint=true and readOnlyHint=false, and the description confirms by saying 'Delete'. It adds context about clearing sensitive data and that it targets previously stored memories, which is useful 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 two sentences, front-loaded with the core action, and includes usage guidance without waste.
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 one-parameter tool with comprehensive annotations and schema, the description covers purpose, usage, and data context. It doesn't discuss error handling, but that's not essential given the tool's simplicity.
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 already describes key as 'Memory key to delete' (100% coverage). The description reinforces by mentioning 'by key' and 'previously stored memory' but does not add substantial new detail.
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' with a specific verb and resource. It clearly differentiates from sibling tools like remember and recall by specifying the deletion action.
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?
It explicitly states when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data' and suggests pairing with remember and recall. It lacks explicit when-not guidance but provides clear contexts.
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.
Multiple tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk) blurs together for an agent trying to pick one. entity_profile and recent_changes also both pull company data, and ai_visibility_check vs scan_competitor_ai_presence are single-vs-multi variants of the same probe.
Naming is mostly snake_case and readable, with many verb_noun forms (validate_iban, generate_llms_txt, resolve_entity). However, there are bare verbs (remember, forget, recall, subscribe, unsubscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and inconsistent prefixes (ask_ vs polymarket_ vs suggest_) that break a clear pattern.
33 tools is borderline-heavy for a data-research API, but the bigger issue is that the server is named Openiban yet contains only two IBAN tools and 31 unrelated Pipeworx/data tools. The count feels bloated and misaligned with the server's apparent identity, though not extreme enough for a 1 or 2.
For the Pipeworx data-research domain the surface is quite rich (query, deep research, entity profiles, comparisons, subscriptions, memory). For the server's stated IBAN purpose, only validate and suggest_iban exist — no generation, parsing, batch checks, or bank detail coverage — so the tool set is severely incomplete relative to the server name.