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. The description adds context about clearing sensitive data, but does not detail behavior on missing keys, error states, or confirmation. Adequate but not exceptional.
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 cover action, usage context, and sibling relationship with no unnecessary words. Front-loaded with the action.
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 tool with one parameter and no output schema, the description is complete: it states purpose, when to use, and related tools. No gaps remain.
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 only parameter 'key' has a clear description in the schema. The description's mention of 'by key' is redundant. No additional meaning beyond schema is provided.
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,' specifying the action (Delete), resource (previously stored memory), and scope (by key). This distinguishes it from siblings like 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?
Provides explicit usage scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' Also pairs with remember and recall. However, it does not explicitly state when not to use or list alternatives beyond siblings.
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.
Most tools are clearly distinct, but ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research overlap with the base router, and the polymarket_* family contains several scanning/arbitrage tools with fuzzy boundaries. The long descriptions help, but an agent could easily call the wrong variant.
There are coherent clusters (pipeworx_*, polymarket_*, ask_pipeworx_*, bare Adzuna verbs), but the overall server mixes snake_case, bare nouns, compound names, and -_prefixed names without a unifying convention. Some tools like compare_entities, entity_profile, and scan_dependency follow a descriptive style that does not match the verb_ noun pattern used elsewhere.
37 tools is well above the 25-tool threshold, and the server named 'Adzuna' includes far more than job-search functionality: prediction markets, memory, subscriptions, npm dependency checks, AI visibility probes, and llms.txt generation. The count feels like a bundled mega-platform rather than a focused job-data server.
For a job-search-focused server, the Adzuna tools cover search, categories, history, regional stats, salary histograms, and top companies, but there is no direct job-detail or application workflow. For the broader Pipeworx research surface, coverage is very thorough, so the main completeness problem is the lack of a clear unified domain rather than a specific missing operation.