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 mark destructiveHint true. Description adds context about clearing sensitive data and pairing with other tools, which is consistent and adds value.
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, front-loaded with action. No wasted 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?
Simple tool with one required parameter and no output schema. Description covers purpose, usage, and paired tools adequately.
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%, so baseline is 3. Description adds no additional semantics beyond the schema's parameter description.
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?
Clearly states 'Delete a previously stored memory by key', using specific verb and resource. Distinguishes from siblings 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 provides when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also pairs with remember and recall as 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.
ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, creating a true duplicate entry point, and the prediction-market cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) all detect mispricings with heavily overlapping descriptions. The detailed docs help, but an agent choosing among these will frequently misselect.
The set mixes verb-first names (ask_pipeworx, compare_entities, discover_tools, subscribe) with noun-first names (polymarket_edges, entity_profile, ip_context, recent_changes) and bare verbs (remember, forget) without a unifying convention. Subfamilies are internally consistent (polymarket_*, ask_pipeworx_*, subscribe/unsubscribe), which keeps it readable, but there is no predictable server-wide pattern.
32 tools is well into the too-many band, and several tools duplicate or wrap others: ask_pipeworx_beta is a redundant copy of ask_pipeworx, scan_competitor_ai_presence wraps ai_visibility_check, and bet_research overlaps polymarket_edges/arbitrage. The broad scope justifies a large set, but it would be tighter and clearer around 20-24 tools.
For the domain the descriptions actually define (structured-data research, company intelligence, prediction markets, subscriptions, memory), coverage is strong with few dead ends: subscription and memory lifecycles are complete, and research has routing/grounded/deep modes. However, the server is named Greynoise while only ip_context serves that domain, and side tools like generate_llms_txt and scan_dependency sit outside any core workflow.