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 indicate destructive and idempotent behavior. Description adds context of clearing sensitive data and confirming deletion of previously stored memory, beyond 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?
Two sentences, no wasted words, clearly structured for quick comprehension.
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 no output schema, the description fully suffices to guide correct use.
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 covers 100% of the single parameter 'key' with description 'Memory key to delete'. Description adds no additional detail beyond schema.
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 the verb 'Delete' and resource 'previously stored memory by key', distinguishing it from siblings like '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 specifies when to use: when context is stale, task done, or clearing sensitive data. Also suggests pairing 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.
Some tools have near-identical purposes (ask_pipeworx vs ask_pipeworx_beta are currently identical; polymarket_arbitrage vs polymarket_edges both find opportunities), but detailed descriptions and distinct input patterns mostly help an agent choose. A few discovery/verification tools (suggest_questions vs discover_tools, validate_claim vs ask_pipeworx_grounded) also overlap, creating residual ambiguity.
All names are snake_case and many use domain prefixes (comtrade_, polymarket_, pipeworx_), but the set mixes verb_noun (compare_entities, discover_tools), bare verbs (remember, forget, subscribe), and noun phrases (entity_profile, recent_changes). This inconsistency, plus the use of domain-specific prefixes as a substitute for a uniform verb_noun style, makes the naming pattern only moderately predictable.
35 tools is well beyond the typical well-scoped range, and the vast majority (prediction markets, memory, subscriptions, feedback, npm dependency checks) have nothing to do with the server's apparent Comtrade trade-data purpose. This bloat makes the set feel unfocused, even though some meta-tools serve a general data-access mission.
For the Comtrade trade-data domain, the four comtrade_* tools cover country codes, top commodities, top partners, and detailed bilateral trade values — enough for most queries, with minor gaps like time-series trends or tariff lookups. For the broader Pipeworx data-access scope, the set is extensive (ask_pipeworx, deep_research, entity_profile, validate_claim, subscriptions), so no severe dead ends are apparent.