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Duvo MCP

by io.github.duvoai

Propose Clarity Landscape Process

proposeClarityLandscapeProcess

Create a manual process in the organization's Process Landscape, either as an unassigned proposal or atomically assigned to an eligible team.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
orgIdYes
teamIdNo
parentIdNo
descriptionNoOne short paragraph (1-3 sentences) saying why this process belongs in the landscape. Use only what you saw in the captures. Say what the process is (don't just repeat the name), show proof it really happens, and name where you heard it - be as specific as the captures allow, like "a warehouse lead said so in their interview" or "it came up in two returns recordings". Use only facts from the captures: never make up sources, people, dates, quotes, or numbers, and don't stretch what was said. If you have no real proof the process happens, don't propose it.
materializationModeNoUse "proposal" to record a process the organization plausibly needs, owned by `teamId` for review, WITHOUT creating a real process record. `teamId` is then required. Chat-scoped discovery agents may use proposal mode for their pinned team; direct human and API callers require Manager-or-above authority for that team. A proposal is idempotent: an equivalent live proposal under the same parent is returned untouched rather than duplicated. Defaults to "auto", which materializes a real process when `teamId` is set — except in a landscape-onboarding chat, which may only propose, and so defaults to "proposal".

TDQS

A3.7/5.0
Behavior4/5

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

The materializationMode schema description discloses substantial behavior beyond the three sparse annotations: proposal mode creates no real process record, proposals are idempotent ('returned untouched rather than duplicated'), authority requirements vary by caller type, and defaults shift in onboarding chats. This adds genuine context that the annotations (readOnly=false, idempotent=false, destructive=false) do not provide. The write semantics align with readOnlyHint=false; the proposal-level idempotency nuance does not contradict the tool-level idempotentHint=false, so no contradiction is flagged.

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

Conciseness4/5

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

The main description is a single front-loaded sentence that wastes no words. The two long parameter descriptions are dense but each sentence carries operational meaning (mode semantics, authority levels, idempotency, anti-fabrication guardrails). The materializationMode text could be restructured for easier parsing, but nothing is padding.

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

Completeness3/5

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

With no output schema and only three boolean annotations, the description carries most of the burden, and it covers the mode/authority/idempotency story well. However, meaningful gaps remain for a creation tool in a complex domain: what makes a team 'eligible,' what `parentId` refers to, and what the tool returns (the created proposal/process) are all unaddressed.

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

Parameters4/5

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

Schema coverage is only 33% (2 of 6 params described), but the two hardest params carry rich semantics: `description` gets detailed sourcing/anti-fabrication guidance, and `materializationMode` explains its enum values, defaults, and authority implications. `teamId` is contextualized in the materializationMode text as the owner-for-review, and `name`/`orgId` are inferable from the tool's purpose, leaving only `parentId` meaningfully unexplained.

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

Purpose4/5

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

The description uses a specific verb+resource ('Create a manual process in the organization's Process Landscape') and immediately distinguishes the two operating modes: unassigned proposal vs. atomically assigned to an eligible team. It partially differentiates from siblings like createClarityProcess and addClarityLandscapeNodePerson through the proposal/assignment framing, but it never names those alternatives explicitly, so it stops short of a 5.

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

Usage Guidelines3/5

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

The main description offers no when-to-use guidance, but the materializationMode schema description provides meaningful context: chat-scoped discovery agents may use proposal mode for their pinned team, direct human/API callers require Manager-or-above authority, and landscape-onboarding chats are restricted to proposals. There is no explicit when/when-not guidance against alternatives like createClarityProcess, and the eligibility criteria for an 'eligible team' are never defined.

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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