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 and idempotentHint=true, so the description's addition about clearing sensitive data is helpful but not essential. There is no contradiction.
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, no fluff, front-loaded purpose. 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 delete tool with one parameter and good annotations, the description is complete. Mentions pairing with remember and recall, providing full context.
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 fully describes the 'key' parameter with a clear description. The description adds minimal value beyond 'by key'. With 100% schema coverage, baseline 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 clearly states 'Delete a previously stored memory by key' with a specific verb and resource. It distinguishes itself from sibling tools by mentioning 'Pair with remember and recall.'
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 explicitly lists three usage contexts: when context is stale, task is done, or to clear sensitive data. It also references sibling tools. However, it does not explicitly state when not to use this tool.
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.
There are several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta (explicitly described as currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying data; the six Polymarket tools (bet_research, arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) heavily overlap in purpose and are easy to confuse. Even detailed descriptions do not fully resolve which tool an agent should pick first.
All names use snake_case and many follow a verb_noun pattern (search_filings, get_filing, list_issue_codes, resolve_entity), but there is a mix of verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), noun-first names (recent_changes, entity_profile, pipeworx_trending), and brand-prefixed families (pipeworx_*, polymarket_*). The naming is readable but not a single predictable pattern.
A server named 'Senate Lobbying' exposes 34 tools, but only three (search_filings, get_filing, list_issue_codes) relate to LDA lobbying data. The remaining 31 cover generic Pipeworx data lookup, prediction markets, memory, subscriptions, AI visibility, and npm package audits—an extreme overreach for the apparent scope and likely to confuse an agent expecting a focused lobbying toolkit.
For the lobbying domain implied by the server name, only search, get-one-filing, and issue-code enumeration exist; there is no aggregation/stats tool, no lobbyist/client entity resolution for LDA, no registrant or foreign-entity browsing, and no coverage of related concepts like lobbying firm hierarchies or spending trends. The generic Pipeworx tools fill a different domain, so the lobbying-specific surface has significant gaps.