Duvo MCP

List Runs

listRuns

Read-onlyIdempotent

List runs for the current team. Supports filtering by agent, user, queue, status, etc. Messages, evaluation data, and queue metadata are included where available. Set count_only=true to skip Run row selection and enrichment. The normal response shape is returned with data: [] and the matching total.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Number of runs per page (1-100, default 20)
`since`	No	Return runs completed at or after this ISO-8601 timestamp. Runs that have not completed are excluded.
`until`	No	Return runs completed before this ISO-8601 timestamp. The upper bound is exclusive. Runs that have not completed are excluded.
`offset`	No	Number of runs to skip
`search`	No	Full-text search across run titles and case titles
`source`	No	Filter to runs created from this source (e.g. api, schedule)
`status`	No	Filter to runs with this status
`sort_by`	No	Field to sort by (default created_at)	created_at
`team_id`	No	Duvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here.
`user_id`	No	Filter to runs owned by this user; non-superadmin callers are scoped to themselves regardless of this value
`agent_id`	No	Filter to runs for this agent
`digest_id`	No
`count_only`	No	Skip row selection and enrichment. The normal list response shape is returned with an empty row array and the matching total.	false
`has_issues`	No	If true, only return runs that have evaluation issues
`sort_order`	No	Sort direction (default desc)	desc
`automation_id`	No	Filter to runs whose agent belongs to this automation
`case_queue_id`	No	Filter to runs associated with this queue
`issue_severity`	No	If set, only return runs whose latest successful evaluation has at least one failing rubric with this severity (critical \| medium \| low). Implies has_issues; legacy evaluations without severity companion fields do not match this filter.

Schema Changelog

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

First observedAug 17, 2026

TDQS

A4/5.0

Behavior4/5

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

The description adds specific behavioral details beyond the annotations: it explains that runs for the current team are returned, that filters are supported, and that messages, evaluation data, and queue metadata are included where available. It also describes the count_only behavior, which alters the response shape. This provides good transparency, though it does not cover every edge case.

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 and well-structured. It states the primary action, lists supported filters, mentions the included data, and explains the count_only behavior in a clear, direct manner. There is no unnecessary verbosity or redundancy.

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 that there is no output schema, the description provides enough context about the response shape: it mentions the normal response with data: [] and the matching total, and notes that messages, evaluation data, and queue metadata are included. This is sufficient for a list operation, though it does not enumerate every field of each run object.

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

Parameters3/5

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

The input schema has high description coverage (94%), and the parameter descriptions within the schema are detailed. The tool description does not add meaningful new information about parameters beyond what the schema already documents. Since the schema covers most parameters thoroughly, the description's contribution to parameter semantics is minimal, matching the baseline of 3.

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's purpose: 'List runs for the current team.' It specifies the resource (runs) and the action (list), and notes that it supports filters and includes additional data like messages and evaluation info. This is distinct from other list tools and unambiguous.

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 description implies usage for listing runs but does not explicitly distinguish it from alternative tools like getRun or listCaseRuns. It mentions the count_only option but does not provide guidance on when to choose this tool over others. The usage is inferable but not explicitly stated.

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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