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Glama

Duvo MCP

by io.github.duvoai

Probe an MCP server for available tools

probeMcpServer

Probe an MCP server URL and list the tools it exposes. Useful as a dry-run before creating a connection — verifies the URL is reachable, that authentication headers (if any) are correct, and surfaces the tool catalog. Performs no writes; sits alongside /v2/teams/:team_id/connections/oauth/mcp/check (which probes the same URL for OAuth support).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headersNoOptional HTTP headers for authentication (raw server_url mode only)
team_idNoDuvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here.
server_urlNoRaw MCP server URL to probe (for custom MCP servers)
integration_slugNoCatalog integration slug. The backend resolves the MCP server URL and dummy auth headers server-side — no server_url or headers needed.

TDQS

A3.6/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotation Contradiction: The description explicitly states 'Performs no writes', but annotations declare readOnlyHint=false. This is a direct conflict about whether the tool mutates state. The description also adds some behavioral context (verifies reachability/auth), but the contradiction undermines trust and forces a score of 1 per the rubric.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the main purpose, then supporting use case and a sibling pointer. No fluff or repetition of schema details. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple probe tool with no output schema, the description covers purpose, use case, safety claim, and sibling alternative. It's largely complete for selection and invocation, but the annotation contradiction creates a credibility gap. Still, given the schema richness, the description is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides detailed descriptions for all four parameters (headers, team_id, server_url, integration_slug) with 100% coverage. The description adds little beyond schema content—mentions auth headers and URL reachability but no new parameter semantics. Baseline 3 applies because the schema carries the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Probe an MCP server URL and list the tools it exposes.' It clearly distinguishes this from the sibling OAuth check by naming the alternative endpoint and its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool ('useful as a dry-run before creating a connection') and what it verifies (reachability, auth headers, tool catalog). It also mentions the OAuth check alternative, providing sibling differentiation. However, it doesn't explicitly state when not to use this tool versus the alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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