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.
Input schema / examplesAdded value: +[
+ {
+ "key": "user_research_topic"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and idempotentHint=true. The description adds useful context by mentioning 'clear sensitive data' and that the memory was saved by the agent earlier, enhancing transparency beyond annotations.
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 purpose. Every sentence adds value: first states function, second provides usage context and tool pairing. No wasted words.
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 a single parameter and no output schema, the description adequately covers purpose, usage, and pairing. Minor gap: does not specify behavior if the key does not exist, but annotations and simplicity compensate.
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% with the parameter 'key' described as 'Memory key to delete'. The description adds no additional semantics beyond confirming it is a memory key, so 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 states 'Delete a previously stored memory by key', providing a specific verb and resource. It distinguishes from sibling tools like remember and recall by explicitly pairing with them.
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 specifies when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also advises to pair with remember and recall, but does not include explicit exclusions.
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.
ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and ask_pipeworx_grounded is another variant of the same router, so an agent can easily misselect. Additionally, bet_research, polymarket_edges, and polymarket_arbitrage all present as prediction-market opportunity finders with overlapping responsibilities.
Most tools follow snake_case verb_noun (ask_pipeworx, list_subscriptions, resolve_entity, unsubscribe, validate_claim), but several break the pattern with noun phrases like polymarket_edges and pipeworx_trending, plus oddities like startup_oracle_evaluate. The mix is readable but not predictable.
32 tools is well over the 25 threshold and the set is not tightly scoped: prediction markets alone account for six overlapping tools, and the server also bundles memory, subscriptions, npm dependency scanning, llms.txt generation, and AI visibility checks. ask_pipeworx_beta adds a duplicate that does not earn its slot.
As a read-heavy research gateway, the surface is broad: lookups, profiles, comparisons, verification, deep research, discovery, and citation handling are covered, and the subscription/memory helpers have their own lifecycle operations. But the 'Startup Oracle' mission is thin — startup-specific evaluation is a single joke tool, and there is no way to update subscriptions or act on research findings beyond saving memory.