Duvo MCP

Get Clarity Process

getClarityProcess

Read-onlyIdempotent

Get the v2 read model for a clarity process: the process row (with operational fields like generation_error, generation_progress, generation_started_at, generation_last_activity_at, custom_prompt), its captures, and the lightweight version arrays for both snapshot tabs. The full payload of any specific snapshot is fetched lazily via the unified per-snapshot detail endpoint (GET .../snapshots/:kind/:id); this read model deliberately doesn't carry it so the response stays small.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`captures`	No	Capture payload mode. `full` (default) embeds each capture's transcript content. `lite` omits `transcript`/`videoTranscript` (returned as null) and relies on the `hasTranscript`/`hasVideoTranscript` flags; fetch content on demand via `GET .../captures/:capture_id`.	full
`process_id`	Yes	The clarity process id

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedAug 17, 2026

TDQS

A4.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description aligns with annotations (readOnlyHint, idempotent, non-destructive) and adds transparency by explaining that the response omits full snapshot payloads to keep the response small. This goes beyond the annotations and sets correct expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, well-structured, and covers purpose, scope, and the lazy-loading caveat without redundancy. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple parameters and the description's detail about the response contents (process row, captures, version arrays), the agent has enough context to use the tool correctly. No output schema is present, but the description fills that gap adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description enriches the 'captures' parameter by explaining the 'full' vs 'lite' modes and their impact on transcript fields. This adds significant meaning beyond the raw enum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets a v2 read model for a clarity process, specifying the contained fields (process row, captures, version arrays). It distinguishes itself from snapshot-specific tools like getClarityProcessSnapshot and getClarityProcessTree.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly mentions that full snapshot payloads are fetched lazily via a unified per-snapshot detail endpoint, guiding agents to use this tool for the lightweight read model rather than trying to retrieve heavy data here. This is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0

Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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