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.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal destructive and idempotent behavior. The description adds context about what gets destroyed (a previously stored memory) and the rationale (clearing sensitive data), which goes beyond the raw annotations. It doesn't detail edge cases like missing keys, but the idempotent hint covers that.
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 three concise sentences, front-loaded with the purpose, followed by usage guidance and complementary tools. Every sentence earns its place with no fluff or redundancy, making it efficient and easy to parse.
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 destructive tool with strong annotations, the description provides purpose, usage triggers, and related tools. The lack of an output schema is acceptable for a delete operation, and the idempotent/destructive hints cover behavioral expectations, so the description is complete.
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 property 'key' described as 'Memory key to delete'. The description's 'by key' merely restates the schema. No additional semantic value is added beyond what the input schema already provides, so the baseline of 3 applies.
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 explicitly states 'Delete a previously stored memory by key', which is a specific verb (delete) and resource (memory by key). It clearly distinguishes from siblings like 'remember' (store) and 'recall' (retrieve), leaving no ambiguity about the tool's function.
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 when-to-use scenarios: 'context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also names complementary tools ('Pair with remember and recall'), giving clear guidance on placement in a workflow.
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 distinct purposes, but subtle overlap exists in the ask_pipeworx variants and multiple Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) which could cause misselection without careful reading of descriptions.
All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures, e.g., ai_visibility_check, compare_entities, resolve_entity. No mixing of conventions.
With 29 tools, the server exceeds the typical ideal range of 3-15 and enters the 'too many' category. While each tool serves a specific purpose, the breadth of functionality could likely be streamlined or consolidated without losing capability.
The tool surface covers a wide range of domains—company research, fact verification, data querying, betting analysis, memory, and subscription management. However, some specialized queries rely on the generic ask_pipeworx tool rather than dedicated endpoints, leaving minor gaps for direct access.