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Glama

Duvo MCP

by io.github.duvoai

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
NameRequiredDescriptionDefault
searchNoFree-text search selecting the cases when all_matching is true.
statusYesTarget 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.
filtersNoFilters selecting the cases when all_matching is true.
case_idsNoExplicit case IDs to act on (1-100). Provide this or set all_matching.
queue_idYesThe queue's unique identifier
all_matchingNoWhen true, act on every case matching the provided filters/search instead of an explicit id list.

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / status / description
      Previous value: -"Target status for each case. `completed` and `failed` are terminal; `pending` resets the case (the queue's trigger consumer, if any, will re-claim it)."New value: +"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."
    • changedInput schema / properties / status / enum
      Previous value: -[
-  "pending",
-  "completed",
-  "failed"
-]New value: +[
+  "pending",
+  "completed",
+  "failed",
+  "canceled"
+]
  2. Changed1 schema field changed
    • addedInput schema / properties / filters / properties / issue_severity
      Added value: +{
+  "items": {
+    "enum": [
+      "critical",
+      "medium"
+    ],
+    "type": "string"
+  },
+  "type": "array"
+}
  3. First observed

TDQS

A4.2/5.0
Behavior5/5

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

With all annotations false, the description carries the full disclosure burden and meets it thoroughly: it reveals that the call interrupts active runs, releases case ownership, preserves human-in-the-loop state (pending requests and approval batches stay answerable), and re-dispatches cases when resetting to pending. This is exactly the kind of side-effect context an agent needs and it does not contradict any annotation.

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, front-loaded sentences: core action first, then side effects, then the pending-specific consequence. Every sentence earns its place, and none repeats information already present in the annotations or schema.

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?

For a 6-parameter bulk mutation with nested filters and no output schema, the description plus the fully-covered schema give an agent almost everything needed: selection semantics, status semantics, and non-obvious side effects are all disclosed. The only gap is the return value/confirmation shape, which neither the description nor an output schema addresses.

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% and the schema already documents status semantics (terminal states, 'success' not settable, pending reset) and selection semantics (case_ids vs all_matching). The description's only additive parameter insight is the trigger-consumer re-dispatch detail for pending, which is a marginal complement to the schema's 'pending resets the case'.

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?

States a specific verb+resource ('Update the status of multiple cases') and enumerates the four settable target states (pending, completed, failed, canceled). This clearly distinguishes it from sibling tools like bulkUpdateCasePriority (priority) and bulkReprocessCases (reprocessing) without needing to inspect their schemas.

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 bulk status-update context is clear and the behavioral consequences are spelled out, but the description never names alternatives (e.g., updateCase for single-case updates, bulkUpdateCasePriority for priorities) or states explicit when-not-to-use conditions. Usage is implied rather than directive.

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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