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?
The annotations already declare destructiveHint=true and idempotentHint=true, so the description doesn't need to restate destructiveness. It adds context about clearing sensitive data and implies the memory must have been stored previously. However, it doesn't clarify edge cases like what happens if the key doesn't exist or whether the deletion is permanent beyond the annotation.
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 concise and well-structured: action first, then usage conditions, then sibling pairing. No unnecessary 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?
The tool is simple with one parameter and rich annotations. The description covers what, when, and with which siblings. However, it does not describe the return value or behavior on missing keys, which could be helpful given no output schema. Slight gap.
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?
The input schema has 100% coverage, describing the key parameter as 'Memory key to delete.' The description also says 'by key,' but doesn't add extra constraints or details. 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 clearly states the tool's function: 'Delete a previously stored memory by key.' It uses a specific verb and resource, and 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 provides explicit conditions for when to use it: 'when context is stale, the task is done, or you want to clear sensitive data.' It also names the related tools (remember and recall), helping the agent choose among them.
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.
Several tools have overlapping purposes, such as the three variants of ask_pipeworx and the multiple prediction market analyzers. While descriptions help differentiate, agents may still struggle to select the correct tool in some cases.
Most tools follow a consistent snake_case verb_noun pattern (e.g., resolve_entity, validate_claim). Minor deviations exist (e.g., forget, remember, recall) but do not significantly harm predictability.
With 35 tools, the server feels heavy for a single named service. The broad scope justifies many tools, but it borders on overwhelming and could benefit from consolidation or clearer grouping.
Despite the name 'disease', the server covers a wide array of domains beyond health. However, while rich in data queries and prediction markets, it lacks obvious tools for domain-specific tasks like disease outbreak tracking or general full-text search.