listCaseRunsList Runs (agent runs) that have claimed or received handover of a case, newest first. Capped at 50 ownership events.
| Name | Required | Description | Default |
|---|---|---|---|
| case_id | Yes | The case's unique identifier |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description correctly adds value beyond them: it discloses ordering ('newest first') and the hard limit ('Capped at 50 ownership events'). These are genuinely useful behavioral traits that the annotations do not express. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero wasted tokens. The core purpose is front-loaded, the parenthetical '(agent runs)' disambiguates terminology against sibling listRuns, and the second sentence adds two concrete behavioral facts (ordering, cap). Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only list tool with rich annotations, this is nearly complete: purpose, scope, ordering, and limit are all stated. The only minor gap is that no output schema exists, so the return shape (what fields each run object contains) is left unspecified — but that is a modest omission for a straightforward list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents case_id ('The case's unique identifier'). The description adds no parameter-level meaning beyond what the schema provides, which matches the baseline-3 expectation when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), a resource ('Runs (agent runs) that have claimed or received handover of a case'), and scoping criteria. The 'claimed or received handover' phrasing clearly distinguishes this tool from generic siblings like listRuns and listCaseRunRecentMessages, so an agent can tell them apart without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the usage context — this is the tool to use when you want runs that own or inherited a specific case, versus generic run listings. However, it never explicitly names alternatives (e.g., listRuns for all runs, getRun for a single run) or states when-not-to-use, leaving dispatch partially to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.