getClarityProcessLandscapeFetch the organization's full process landscape with process summaries, team assignments, and page-level stats
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| rootId | No | ||
| teamId | No |
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds only what data is returned and does not disclose behavioral details such as filtering effects of rootId/teamId, pagination, rate limits, or response shape. It aligns with annotations, so no contradiction exists.
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?
The description is a single sentence that front-loads the verb and object, lists meaningful content components, and contains no filler. Every word contributes to communicating the tool's purpose.
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 tool with no output schema and no parameter descriptions, this description is thin. It names high-level return contents but does not explain optional parameters, response structure, or when to use this instead of closely related Clarity tools. An agent could make a basic call with orgId but would lack enough context to correctly use rootId/teamId or understand the full return.
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?
The schema has 3 parameters with 0% description coverage, and the description does not compensate. It indirectly associates orgId with 'organization,' but rootId and teamId are left entirely unexplained, forcing the agent to infer their semantics solely from their names.
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?
Description uses a specific verb 'Fetch' and names the resource 'organization's full process landscape' with concrete contents: process summaries, team assignments, and page-level stats. It is clear, but it does not explicitly distinguish itself from related siblings like getClarityProcessTree, listClarityProcessSummaries, or generateClarityProcessLandscape.
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 phrase 'full process landscape' implies this is used when a broad org-wide view is needed, but no explicit when-to-use or when-not-to-use guidance is provided. The description does not mention alternatives or state exclusions, leaving the choice between this tool and similar Clarity read/list/generate tools 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.