generateClarityProcessLandscapeStart a process-landscape generation run for the organization from its eligible Clarity captures (organization executives and owners)
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes |
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 convey that this is not read-only, not idempotent, and not destructive; the description adds that the run draws on eligible captures from organization executives/owners, which is useful scoping. It does not disclose side effects, return value, or async behavior, but the annotation set lowers the burden.
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?
One sentence, front-loaded with the core action, and no filler. The parenthetical 'organization executives and owners' is compact but slightly ambiguous, so it is not a perfect 5.
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?
The description does not state what the tool returns, whether the generation run is asynchronous, how the agent can monitor or retrieve the result, or what prerequisites must be true before starting. For a low-complexity tool with no output schema, this leaves meaningful gaps.
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?
With a single parameter (orgId) and 0% schema description coverage, the description only indirectly maps to it by saying 'for the organization.' This gives the agent the basic semantic link but does not explicitly explain orgId format, source, or how it affects eligibility.
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 uses a specific verb ('Start') and a specific resource ('process-landscape generation run'), and it adds an input source ('eligible Clarity captures'), so an agent can tell this is the initiating tool for landscape generation rather than, say, getClarityProcessLandscape. It does not explicitly name or differentiate against a sibling like generateClarityProcessSnapshot, which would push it to a 5.
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 context is clear: it is used to start a landscape-generation run for an organization based on eligible Clarity captures. However, it gives no explicit when-to-use vs alternatives, no preconditions beyond eligibility, and no mention of when a different generation or snapshot tool should be chosen.
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.