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 and idempotentHint, and the description adds context about what gets deleted (a previously stored memory) and the motivation (clearing sensitive data). No contradiction with annotations; the added context goes beyond the structured hints.
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, front-loaded with the core action, and the second sentence provides actionable guidance. Every sentence earns its place with no wasted words or repetition.
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 one-parameter delete operation, the description covers purpose, usage context, and pairing with siblings. Annotations handle safety implications (destructive, idempotent), and no output schema is needed. The tool is fully contextualized.
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 fully describes the 'key' parameter with 100% coverage, so the baseline is 3. The description merely restates 'by key' without adding any additional semantic detail 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?
The description states 'Delete a previously stored memory by key' which clearly identifies the action and resource. It distinguishes from siblings like 'remember' and 'recall' by specifying deletion, making the purpose unambiguous.
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 states when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data.' It also recommends pairing with 'remember' and 'recall,' naming the relevant sibling tools and providing clear 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 tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as entry points for data questions, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. Entity-focused tools (entity_profile, compare_entities, recent_changes, ai_visibility_check, scan_competitor_ai_presence) also blur together even with lengthy descriptions.
All tools use lowercase snake_case, which is consistent and readable. There is some variation between verb-first names (discover_tools, resolve_entity) and noun-first names (entity_profile, polymarket_arbitrage), plus the ask_pipeworx variant family, but the pattern is predictable overall.
35 tools is heavy, and the scope sprawls far beyond the 'Rba' server name: only 4 tools relate to the Reserve Bank of Australia, while the rest cover a universal data router, prediction markets, memory, subscriptions, AI visibility, npm scanning, and more. Several meta-tools (ask_pipeworx, deep_research, discover_tools, suggest_questions) duplicate the discovery/routing role, making the count feel inflated.
For the RBA-specific subdomain, coverage is solid: directory lookup, series fetching, cash rate, and exchange rates. For the broader data-research domain most tools imply, coverage is quite comprehensive (routing, grounded answers, entity resolution, fact-checking, monitoring, memory), but the server's stated identity is unclear, and the beta duplicate tool adds noise rather than filling a real gap.