checkMcpOAuthProbe an MCP server URL to discover whether it supports OAuth Dynamic Client Registration. Returns the authorization endpoint and required scopes when supported. Useful as a precursor to /v2/teams/:team_id/connections/oauth/mcp/start or /v2/teams/:team_id/connections. Performs no writes.
| Name | Required | Description | Default |
|---|---|---|---|
| mcp_server_url | Yes | URL of the MCP server to probe for OAuth support. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotation contradiction: readOnlyHint is false, implying the tool may modify state, yet the description states 'Performs no writes.' This sends conflicting signals to an agent. It also does not describe error behavior for unsupported URLs, which is relevant for a probe tool.
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 tightly scoped sentences deliver purpose, return value, usage context, and safety in under 40 words. No fluff or repetition.
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 one-parameter probe with no output schema, the description is fairly rich (return value, usage, safety). However, the contradictory annotation undermines completeness, and it does not state what happens when OAuth is unsupported (e.g., error vs. empty response).
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 coverage is 100% and the single parameter mcp_server_url is already described as 'URL of the MCP server to probe for OAuth support.' The description restates this without adding format, constraints, or validation semantics, so it provides no additional value beyond the schema.
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 opens with a specific verb ('Probe') and a resource ('MCP server URL') with a clear goal: discover OAuth Dynamic Client Registration support. It distinguishes itself from sibling tools like probeMcpServer by focusing on OAuth-specific discovery.
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?
It explicitly frames the tool as a precursor to specific connection endpoints, giving a clear when-to-use context. However, it doesn't mention alternatives (e.g., probeMcpServer) or when not to use it, so it misses exclusion guidance.
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.