getClarityCaptureGet a single Clarity v2 capture including full transcript and video transcript. Use this when the process was loaded with captures=lite, or when only one capture's transcript is needed.
| Name | Required | Description | Default |
|---|---|---|---|
| capture_id | Yes | The capture id | |
| process_id | Yes | The clarity process id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds that the result includes full and video transcripts, which is useful return-value context. However, it does not elaborate on potential rate limits, auth requirements, or response structure beyond what annotations already convey. It is not misleading and adds some value, so a 3 is appropriate.
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 two sentences: the first states the core action and included content, the second gives usage guidance. Every word earns its place, with no redundancy or filler. It is front-loaded with purpose and immediately provides decision-relevant context.
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 read-only get-by-id tool with full schema coverage and safety annotations, the description covers the essential purpose, return content, and usage conditions. There is no output schema, but the mention of 'full transcript and video transcript' gives a reasonable expectation of the return value. It could be more complete by naming a sibling list endpoint, but it is sufficiently complete for an agent to decide 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 description coverage is 100%: both process_id and capture_id are described with UUID format and basic meaning. The description itself does not add any new parameter-specific details beyond the schema. Since the schema already documents the parameters well, the baseline of 3 is correct.
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 specifies a clear verb ('Get'), a specific resource ('a single Clarity v2 capture'), and the key included content ('full transcript and video transcript'). It also distinguishes itself from listing endpoints by explicitly stating 'single', and the usage context ('process was loaded with captures=lite') helps differentiate from bulk-fetch alternatives.
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 explicitly states when to use the tool: 'when the process was loaded with captures=lite, or when only one capture's transcript is needed.' This gives clear context, though it does not name a specific alternative tool (e.g., a list captures endpoint) for the opposite scenario. The guidance implies 'do not use this for multiple captures' but could be more explicit.
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.