Duvo MCP

Bulk Update Case Status

bulkUpdateCaseStatus

Update the status of multiple cases to pending, completed, failed, or canceled. Interrupts any active runs and releases their case ownership, but never cancels their human-in-the-loop state — pending requests and open approval batches stay answerable/resolvable from the run view. Resetting to pending re-dispatches cases to the queue's trigger consumer.

Input Schema

TableJSON Schema

Name	Required	Description
`search`	No	Free-text search selecting the cases when all_matching is true.
`status`	Yes	Target status for each case. `completed`, `failed`, and `canceled` are terminal — `canceled` records a deliberate human stop, distinct from a system `failed`. `pending` resets the case (the queue's trigger consumer, if any, will re-claim it). `success` is not settable: it means an evaluation passed.
`filters`	No	Filters selecting the cases when all_matching is true.
`case_ids`	No	Explicit case IDs to act on (1-100). Provide this or set all_matching.
`queue_id`	Yes	The queue's unique identifier
`all_matching`	No	When true, act on every case matching the provided filters/search instead of an explicit id list.

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the annotations, the description reveals meaningful side effects: interrupts active runs, releases case ownership, preserves human-in-the-loop state, and re-dispatches on reset to pending. This substantially exceeds what readOnlyHint/idempotentHint/destructiveHint convey.

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?

Three dense, purposeful sentences. The first sentence front-loads the primary action, and the next two add essential side-effect details without redundancy or filler.

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?

The description plus schema covers purpose, side effects, status semantics, and selection options well. It does not mention return/result behavior, and there is no output schema, so a small completeness gap remains.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds some context around status behavior, but the parameters themselves (queue_id, case_ids, filters, all_matching, search) are already well explained in the schema.

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: 'Update the status of multiple cases' and enumerates the four settable statuses. It also identifies a distinctive side effect (interrupting active runs and releasing case ownership), which distinguishes it from sibling tools like bulkUpdateCasePriority and bulkReprocessCases.

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 gives clear behavioral context for when to use the tool, including what happens to active runs and how pending re-dispatches cases. It does not explicitly name alternatives or state when-not-to-use conditions, so it stops short of a 5.

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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