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 declare destructive and idempotent behavior, so the description adds value by specifying what is destroyed ('a previously stored memory') and why it might be used ('clear sensitive data'). This adds context beyond the annotations without contradicting them.
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-loads the core action, and contains no filler. Every sentence contributes purpose or usage context, making it highly efficient.
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 deletion tool with rich annotations (destructive, idempotent), the description covers purpose, usage, and parameter context. No output schema exists, and the description need not explain return values. The tool is fully specified.
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 input schema describes the only parameter 'key' as 'Memory key to delete', covering 100% of parameter semantics. The description redundantly mentions 'by key' but adds no additional parameter detail, so the 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 clearly states the action ('Delete a previously stored memory by key') with a specific verb and resource. It distinguishes itself from sibling tools like 'remember' (store) and 'recall' (retrieve) by making the delete operation explicit.
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 use cases: 'when context is stale, the task is done, or you want to clear sensitive data'. It lacks an explicit when-not or alternative tool mention, but the single-purpose nature of the tool makes the guidance sufficient.
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 are near-duplicates or have fuzzy boundaries: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, the raw data tools (daily_data, hourly_data, event_data, latest, reservoirs) all read as generic 'get data' operations, and the five polymarket_* scanners overlap in opportunity-finding. The verbose descriptions help for many composite tools, but an agent can still easily select the wrong variant.
The naming is predominantly consistent lowercase snake_case with strong prefixed families (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*) and clear verb_noun actions. Minor deviations like noun-only latest/reservoirs, ask_pipeworx lacking a separator, and generate_llms_txt keep it from a 5.
37 tools is well above the heavy threshold, and the count is inflated by redundant meta-tools, three router variants, six overlapping generic data fetchers, and six prediction-market tools. Many tools are purposeful, so it is not an extreme mismatch, but the surface would be much cleaner at roughly 20-25 tools.
The set covers the core data lifecycle well: discovery (discover_tools, suggest_questions), lookup (ask_pipeworx), grounding/validation (ask_pipeworx_grounded, validate_claim, search_within), entity workflows (resolve_entity, entity_profile, recent_changes, compare_entities), plus memory and subscription CRUD. Minor gaps like no explicit fetch-by-citation tool and a limited subscription type set prevent a 5.