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 provide destructiveHint=true and idempotentHint=true. Description adds 'clear sensitive data' but does not substantially expand beyond what annotations convey. No additional behavioral details like permissions or side effects.
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?
Two sentences with no fluff. First sentence states purpose, second gives usage guidance. Every word earns its place.
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 tool with one parameter and full schema coverage, the description is complete. It explains what it does, when to use, and related tools. No gaps.
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 one parameter 'key' described as 'Memory key to delete.' The description does not add any extra meaning beyond the schema, so 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 'Delete a previously stored memory by key,' which specifies the verb (delete) and the resource (memory by key). It distinguishes from siblings by mentioning 'Pair with remember and recall.'
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?
Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also names alternatives/related tools: 'Pair with remember and recall.'
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.
Multiple tools have overlapping purposes, particularly the various data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, etc.) that share similar functionality with subtle differences. The detailed descriptions help but the boundaries are not always clear, making it hard for an agent to reliably select the correct tool.
The naming follows a mix of patterns: some tools use consistent verb_noun (subscribe, unsubscribe, remember, recall) but others are inconsistent (ask_pipeworx vs deep_research vs entity_profile). The main data tools have a common prefix but diverge in style, and the presence of tools like passive_aggression_detect and generate_llms_txt adds further inconsistency.
With 32 tools, the server feels bloated. Many tools could be consolidated (e.g., the ask_pipeworx variants, the Polymarket tools). The inclusion of tangential tools like passive_aggression_detect and generate_llms_txt suggests scope creep. A typical well-scoped server would have 10-15 tools.
The server covers a wide range of data access and some auxiliary functions (memory, subscriptions, feedback), but there are notable gaps like user authentication and data visualization. The addition of an unrelated sentiment analysis tool makes the surface feel incomplete for a focused data server.