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Duvo MCP

by io.github.duvoai

Respond To Human Request

respondToHumanRequest

Respond to a human-in-the-loop request. Use 'approved' (true/false) for approval-type requests, or 'answers' ({question: answer}) for question-type requests. Only works when the run is in 'waiting' status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYesThe run's unique identifier
answersNoFor question-type requests: a map of question text to answer. Multi-select answers should be comma-separated.
approvedNoFor approval-type requests: true to approve, false to deny
request_idYesThe human request's unique identifier

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations by mentioning the 'waiting' status restriction and the conditional parameter usage. It does not elaborate on error handling or side effects, but the annotations already indicate read/write characteristics, so this is sufficient.

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 concise (two sentences) and well-structured, without redundant information. It front-loads the main action and immediately clarifies parameter usage, making it easy to parse.

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

Completeness4/5

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

Given the lack of an output schema, the description adequately covers the core aspects: what it does, how to use it, and when it works. It does not mention potential edge cases (e.g., simultaneous 'approved' and 'answers'), but these are not critical for successful invocation.

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?

The schema provides full descriptions for all parameters (100% coverage), and the tool description adds value by explaining when to use 'approved' vs 'answers' based on request type. This goes beyond the schema's individual parameter descriptions, offering practical guidance.

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 action ('Respond to a human-in-the-loop request') and specifies the exact resource and condition. It distinguishes itself from sibling tools by focusing on human-in-the-loop requests and the 'waiting' status, making it unambiguous.

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

Usage Guidelines4/5

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

The description provides a clear precondition ('Only works when the run is in 'waiting' status') and explains how to use the approved vs. answers parameters. However, it does not explicitly state when to choose this tool over others (e.g., 'use this when a human request is pending'), but the context makes it inferable.

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.

Resources