refreshQueueAggregationForce a fresh recompute of an aggregation definition.
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | The queue's unique identifier | |
| definition_id | Yes | The aggregation definition's identifier |
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 establish this is a mutating, non-idempotent operation, and the description adds the 'force fresh' semantics — implying it bypasses staleness checks and recomputes regardless of current state. However, it does not disclose whether the recompute is synchronous, whether it is expensive, what happens to the previously stored result, or what the response contains.
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?
Seven words in a single sentence with the action verb 'Force' front-loaded. There is no filler, and every word contributes meaning.
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?
With no output schema, the description should explain what a successful refresh returns, but does not. Combined with unresolved sibling ambiguity (evaluate vs refresh vs get result) and no mention of sync/async behavior or side effects on existing results, an agent is left guessing about the consequences of calling this tool.
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 description coverage is 100%, so both queue_id and definition_id are fully documented in the schema with identifiers and formats. The description adds no parameter-level meaning, which matches the baseline 3 since the schema carries the full burden.
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 states a specific verb ('Force a fresh recompute') and a specific resource ('aggregation definition'), making the core action unambiguous. However, it does not distinguish itself from siblings like evaluateQueueAggregation or getQueueAggregationResult, so an agent must infer the difference between refreshing, evaluating, and reading a result.
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?
The description gives no guidance on when to use this tool versus siblings such as evaluateQueueAggregation, getQueueAggregationResult, or createQueueAggregation. There are no exclusions, no conditions, and no mention of preconditions (e.g., the definition must already exist), leaving selection entirely to inference.
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.
Despite detailed descriptions, many tool names are highly ambiguous, with multiple tools covering the same conceptual actions (e.g., acceptClarityCaptureSuggestion vs. acceptClarityTeamAssignmentSuggestion, or the many deleteClarity*Interview tools). The set is so large that distinguishing between, say, listClarityFolders, listClarityProcesses, and listClarityProcessSummaries requires reading deep into descriptions, reducing agent selection accuracy.
The naming convention is predominantly verb_noun (e.g., createClarityProcess, listAgents, deleteQueue), and is remarkably consistent across the 316 tools. There are only minor deviations, such as 'fileSuggestedClarityProcesses' (verb + adjective noun) and 'bulkUpdateCasePriority' (where 'bulk' could be seen as a prefix), but overall the pattern holds strongly.
With 316 tools, this server is extremely oversized for any single agent to manage effectively. The massive number of tools suggests poor modularization—many of these tools likely belong in separate, smaller servers focused on specific domains (e.g., Clarity, Pulse, Agent management). The cognitive load for an agent to choose from 316 options is very high, leading to frequent misselection.
The tool surface covers an extraordinarily wide range of operations across the Duvo platform: agents, runs, cases, queues, Clarity processes, skills, integrations, notifications, teams, and more. Most resource types have full CRUD and lifecycle management. Notable minor gaps exist (e.g., no tools for managing specific notification batch severities dynamically, and some interview management is missing batch operations), but for the platform's scope, coverage is impressively thorough.