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 disclose destructive and non-read-only behavior. The description adds context that it is for clearing stale or sensitive data and pairs with remember/recall, which is useful beyond the structured hints. It does not contradict 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?
The description is two sentences, front-loaded with the action, and then gives usage context and related tool pairings. Every word earns its place with no fluff.
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 tool with annotations and no output schema, the description adequately covers what the tool does, when to use it, and mentions related tools. It lacks a note on return value or idempotency, but the annotations cover safety, and the use cases are explicit.
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 covers the sole parameter 'key' with description 'Memory key to delete' (100% coverage), so the description adds little semantic value. The phrase 'by key' is redundant with the schema, and no additional syntax or format details are provided.
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' with a specific verb and resource, clearly distinguishing it from siblings like remember and recall. It also explicitly mentions pairing with these tools to consolidate the purpose.
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?
'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier' gives explicit context and trigger conditions. However, it lacks explicit when-not-to-use guidance or alternative tools (though it names complements), preventing a 5.
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 ask_pipeworx, ask_pipeworx_grounded, and deep_research all answering questions with slight differences. Also, entity_profile and compare_entities both retrieve company data, and the multitude of Polymarket tools can be confusing. However, many tools have distinct use-cases, so the ambiguity is moderate.
Tool names mix consistent patterns (e.g., get_air_quality, get_apod) with less predictable ones (e.g., pipeworx_feedback, polymarket_arbitrage, bet_research). Some follow verb_noun, others are noun_verb or just noun. The inconsistency is noticeable but not chaotic.
With 34 tools, the server feels overloaded for a 'science' domain. Many tools are dedicated to prediction markets (Polymarket) and finance, which seem tangential. The count could be reduced by merging similar query tools or removing domain-specific betting tools to better fit the scientific theme.
While the server covers a broad range of data sources (SEC, FDA, FRED, etc.), it lacks core scientific tools for physics, chemistry, or biology. The few science-themed tools (get_apod, get_earthquakes) are minor. The set feels incomplete for a dedicated science server, with emphasis on finance and betting instead.