declineClarityLandscapeNodeReject a proposed process landscape node while it is still a proposal, removing it and any proposed descendants. Real processes nested underneath survive and move back to Unsorted. Only proposals can be declined — an accepted node is removed with deleteClarityLandscapeNode.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| nodeId | Yes | The proposed node to reject. |
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 are thin (readOnly=false, destructiveHint=false, no idempotency), so the description carries the disclosure burden and succeeds. It reveals the cascade effect (proposed descendants are removed), what survives (real processes), and their relocation (back to Unsorted). There is mild tension between destructiveHint=false and the removal behavior, but the description's explicit scoping to transient proposals resolves rather than contradicts the annotation.
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?
Three sentences with zero waste: primary action and effect, survival/relocation behavior, then the exclusion condition with the named alternative. The most decision-relevant information is front-loaded, and nothing restates the tool's name or title.
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 two-UUID-parameter tool with no output schema, the description covers the action, cascade behavior, survival of real processes, relocation to Unsorted, and the proposal-only restriction. The only gaps are what the call returns and behavior on an invalid or already-declined node — minor given the annotations and explicit state restriction.
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 only 50%: nodeId has a description but orgId is undocumented in both the schema and the tool description. The description adds the key state constraint ('Only proposals can be declined'), reinforcing nodeId's semantics, but it does not compensate for the undocumented orgId or clarify how the two parameters relate.
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?
States a precise verb and resource: 'Reject a proposed process landscape node.' It immediately scopes the operation to proposal state and names the sibling it is not (deleteClarityLandscapeNode for accepted nodes), so an agent can distinguish it from acceptClarityLandscapeNode and deleteClarityLandscapeNode 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?
Explicitly states the condition for use ('Only proposals can be declined') and names the alternative tool for the other case ('an accepted node is removed with deleteClarityLandscapeNode'). This gives both positive and negative routing guidance, leaving no inference required.
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.