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 destructiveHint=true and readOnlyHint=false, so the description's 'Delete' verb is consistent. The description adds context about clearing sensitive data, but no additional behavioral traits beyond what annotations provide. No contradiction.
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?
Three sentences with no wasted words: first sentence states the action, second provides usage context, third mentions related tools. Efficient and front-loaded.
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, the description covers purpose, usage, and relationships. No output schema needed. Complete enough for an agent to select and invoke correctly.
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 ('by key') adds no new meaning beyond the schema. 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 uses a specific verb ('Delete') and resource ('previously stored memory by key'), clearly stating the tool's function. It also distinguishes itself from sibling tools like 'remember' and 'recall' in the last sentence.
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'. Mentions pairing with 'remember' and 'recall'. Lacks explicit when-not cases, but still offers clear guidance.
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: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta is currently exactly the same), and scan_competitor_ai_presence is a multi-entity wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong variant when a simple lookup is needed.
Tool names mix verb-first patterns (ask_pipeworx, resolve_entity, scan_dependency, validate_claim) with noun-first patterns (denver_layers, entity_profile, recent_changes, pipeworx_trending, polymarket_edges). There are clear families (ask_pipeworx_*, denver_*, polymarket_*, pipeworx_*) but no single consistent verb_noun convention across the set.
34 tools is a large surface for one server, exceeding the 25-tool threshold where coherence starts to degrade. Many tools are meta-routers or near-duplicates (ask_pipeworx family), and the mix of general data access, Denver-specific queries, prediction-market analysis, memory, and subscriptions feels sprawling rather than tightly scoped.
The tool surface covers the apparent domain well: universal data lookup, grounded evidence, deep research, entity resolution, company profiles, comparisons, claim validation, AI visibility, dependency scanning, prediction-market analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct update tool for subscriptions, no way to inspect the full 5,798-tool catalog locally without routing through ask_pipeworx), but agents can accomplish most workflows without dead ends.