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, covering the safety profile. The description adds 'previously stored' and the sensitive-data use case, but no additional behavioral details like error handling or confirmation output. With annotation coverage, this meets baseline but adds limited new info.
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?
Three concise sentences: the first states the action, the second gives usage contexts, and the third references related tools. No fluffy or redundant content.
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 single-parameter delete tool with no output schema, the description covers purpose, usage contexts, and related tools. Annotations cover destructive and idempotent behavior, making it fully sufficient for an agent to select and invoke correctly.
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 a clear parameter description ('Memory key to delete'). The description's 'by key' does not add meaning beyond the schema, 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 uses a specific verb 'Delete' with a clear resource ('previously stored memory') and scope ('by key'), which distinguishes it from sibling tools like remember (store) and recall (retrieve).
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?
It explicitly states when to use the tool ('context is stale, the task is done, or clear sensitive data') and names companion tools ('Pair with remember and recall'), providing clear context and alternatives.
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.
Many tools have overlapping functionality, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which are essentially the same with minor differences. The polymarket_* family also has five tools with similar names and purposes, making it easy to select the wrong one despite detailed descriptions.
Tool names mix verb-first patterns (ask, generate, list, remember) with noun-first patterns (entity_profile, polymarket_arbitrage), and include camelCase like ai_visibility_check. This inconsistent naming style makes the set feel arbitrary and harder to navigate.
With 36 tools, the server exceeds the typical well-scoped range of 3-15. While the multi-purpose nature justifies a larger set, the presence of many near-duplicates (beta/grounded variants, multiple polymarket tools) inflates the count without proportional functional gain.
The toolset covers a wide array of domains including translation, entity resolution, financial data, prediction markets, memory, subscriptions, and AI visibility. It appears very comprehensive for its intended multi-purpose server, with no obvious major gaps in core capabilities.