getQueueAggregationResultRead an aggregation definition's cached result.
| 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 the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description's burden is reduced. The description adds one useful behavioral fact beyond annotations: the result is 'cached,' meaning it may be stale relative to a freshly evaluated aggregation. It does not disclose what happens when no cached result exists or whether the result is returned in the same shape as an evaluation.
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?
The description is a single eight-word sentence with zero filler. The verb is front-loaded, and every word earns its place — 'cached' carries the key semantic weight that separates this from evaluation tools.
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 two-parameter read operation with rich annotations (read-only, idempotent, non-destructive) and no output schema, the description is mostly sufficient. The notable gap is the unresolved relationship to the sibling cluster listingQueueAggregations, evaluateQueueAggregation, and refreshQueueAggregation — an agent must infer when the cached result is the right choice versus those alternatives.
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%: both queue_id and definition_id have type, format, pattern, and descriptive text in the schema. The description adds no new parameter information, so the baseline of 3 applies — the schema carries the full load and does so adequately.
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 names a specific verb ('Read') and resource ('an aggregation definition's cached result'), which is clear and unambiguous. The qualifier 'cached' hints at a distinction from the sibling tools evaluateQueueAggregation and refreshQueueAggregation, but it does not explicitly name them, so differentiation is implied rather than stated.
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 word 'cached' implies this tool is for retrieving a precomputed result rather than computing or refreshing one, giving an implied usage context. However, there is no explicit when-to-use guidance, no mention of alternatives like evaluateQueueAggregation for fresh results or refreshQueueAggregation to recompute, and no exclusion criteria.
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.