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.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and idempotent behavior. Description adds context about clearing sensitive data, which is valuable 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, front-loaded with verb, no wasted words. Efficiently conveys all necessary information.
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 delete tool with one parameter and full annotations, the description covers purpose, usage, and pairing. No gaps present.
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%, and the parameter description is clear. The description does not add extra meaning beyond what the schema provides.
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 deletes a memory by key. It explicitly pairs with 'remember' and 'recall', distinguishing it from siblings.
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?
Provides clear when-to-use scenarios (stale context, task done, clear sensitive data) and references sibling tools. Lacks explicit when-not-to-use, but positive guidance is strong.
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 tool clusters have genuinely blurry boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly 'currently matches ask_pipeworx exactly'), ask_pipeworx_grounded, and deep_research all route questions to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on 'should I bet / where is the edge'. The descriptions are detailed and cross-reference each other, but an agent would still struggle to pick correctly among near-duplicates.
Everything is uniformly snake_case and readable, but there is no consistent verb_noun pattern: verb_noun (resolve_entity, discover_tools, validate_claim) mixes with noun_noun (domain_search, entity_profile, polymarket_arbitrage), adjective_noun (deep_research, recent_changes), bare verbs (forget, recall, remember), and brand prefixes (pipeworx_*, ask_pipeworx_*). Readable, but patternless.
34 tools is well above the heavy threshold, but the bigger issue is scope incoherence: a server named 'Hunter' dedicates only 3 of 34 tools to Hunter.io email lookup while the remaining 31 belong to an unrelated Pipeworx data-research/prediction-market platform. The count is not earned by a single coherent purpose.
The Pipeworx research core is quite thorough: entity resolution, single-lookup, grounded answers, deep research, profiles, comparisons, claim validation, semantic search-within-records, change feeds, subscriptions, and memory form a full research lifecycle. However, the domain is a grab-bag spanning email finding, npm dependency checking, prediction markets, and data research, and the three Hunter.io tools that match the server name are only a thin fragment of the surface.