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 readOnlyHint=false, so the agent knows this is a destructive mutation. The description adds use-case context and confirms the key refers to a previously stored memory, but it does not add details about irreversible deletion or behavior on non-existent keys, which the idempotentHint annotation vaguely covers.
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 succinct sentences, front-loaded with the action verb, and each clause serves a purpose: defining the operation, providing usage triggers, and pointing to related tools. No unnecessary information.
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 one-parameter destructive tool with annotations covering idempotency and destructiveness, the description supplies the essential context: what it deletes, when to use it, and related tools. It does not mention the return value or what happens on missing keys, but these are less critical given the minimal schema and annotations.
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 description coverage is 100% ('Memory key to delete'), so the schema fully documents the 'key' parameter. The description adds no new parameter semantics; it only restates 'by key', which the schema already provides.
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 the verb 'Delete' and identifies the resource 'previously stored memory by key', clearly distinguishing it from sibling tools like remember and recall. It is specific and unambiguous about the operation.
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 states when to use the tool ('context is stale, the task is done, or clear sensitive data') and names related tools (remember and recall) as alternatives. It does not explicitly say when not to use it, but the guidance is clear enough.
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 have carefully written distinctions, but several overlap in purpose: ask_pipeworx versus ask_pipeworx_beta are currently functionally identical, and ask_pipeworx, deep_research, validate_claim, and the Polymarket research tools all sit on the same factual-question axis. The long descriptions help an agent choose, but the set still has multiple ambiguous boundaries.
Names are uniformly snake_case and mostly readable, with clear prefix families like pipeworx_*, polymarket_*, and ask_pipeworx*. However, the verb-noun pattern is inconsistent: many tools are noun phrases (entity_profile, recent_alerts, polymarket_edges) and some are bare verbs (remember, recall, forget), so the naming is not predictable across the full set.
35 tools is well above the 25-tool threshold and feels like an organic platform dump rather than a curated server. The broad data-platform scope partly justifies the number, but the presence of near-duplicate entry points and one-off utilities (generate_llms_txt, ai_visibility_check, scan_dependency) makes the set feel bloated rather than cohesive.
For a read-heavy data/research platform the surface is unusually complete: discovery, single-lookup, grounded-answer, deep-research, entity resolution, comparison, change-tracking, subscriptions, memory, and feedback are all covered. Missing write/execution capabilities like placing trades or modifying BIS flows are reasonable absences for this kind of server; the main gap is a dedicated historical/trend utility beyond the general router.