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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint=true, so the description does not need to repeat that. It adds context about why to use it (clear sensitive data) but does not disclose behavior for missing keys or side effects beyond deletion.
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, zero wasted words. The first sentence states the action, the second adds usage guidance. Efficient and well-structured.
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?
Given the tool's simplicity (one parameter, no output schema, annotations present), the description is fully complete. It covers what, when, and how, leaving no ambiguity for a retrieval-augmented agent.
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% and the description of the 'key' parameter is identical to the schema's description. No additional semantic value beyond what the schema provides, so 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 uses a specific verb ('Delete') and resource ('previously stored memory') with the method ('by key'), making the action unmistakable. It also implicitly distinguishes from siblings 'remember' and 'recall' by focusing on deletion.
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 (stale context, task done, clear sensitive data) and pairs with related tools ('remember' and 'recall'), providing good context. Lacks explicit when-not-to-use, but that is clearly implied.
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.
Many tools occupy clearly different niches, but ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research heavily overlap the same router concept, and bet_research/polymarket_edges/polymarket_arbitrage all target similar 'find an edge' territory. An agent would need to read very long descriptions carefully to avoid selecting the wrong tool.
Names are consistently lowercase snake_case and usefully grouped by prefixes like bnm_, polymarket_, and pipeworx_, which makes the set fairly scannable. However, conventions mix verb-first names (ask_, discover_, resolve_, validate_) with noun-phrase names (entity_profile, recent_alerts, recent_changes), so it is not a uniform verb_noun pattern.
36 tools is well beyond the typical well-scoped 3-15 tool range, and the server bundles several distinct domains: BNM data, the Pipeworx research platform, prediction-market analysis, and memory/subscription management. This breadth would be better split into separate focused servers.
The BNM-specific surface is well covered, with dedicated tools for the main series plus a generic bnm_endpoint passthrough for anything else. The broader data side is also unusually complete, with routing, grounded answers, deep research, entity resolution, profiles, comparisons, and claim verification; only minor gaps remain, such as no dedicated historical endpoint for some BNM series.