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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior1/5

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

The claim 'Performs no writes' directly contradicts readOnlyHint=false in the annotations — the mirror image of the create_record calibration example. It also implies repeatable, side-effect-free behavior that sits uneasily with idempotentHint=false. The rest of the description (reachability check, auth verification, catalog surfacing) is genuinely useful, but the contradiction rule requires score 1.

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 with no filler: function, use case plus verifications, and sibling differentiation. The core action is front-loaded in sentence one and every subsequent clause earns its place.

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

Completeness3/5

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

For a tool with two parameter modes, no output schema, and a vague return description ('tool catalog'), the definition is adequate but gappy: it doesn't say how to pick server_url+headers vs integration_slug, doesn't describe the response shape, and the no-writes claim conflicts with the annotations, leaving the safety profile ambiguous. The strong schema descriptions compensate partially but not fully.

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?

Schema description coverage is 100% with strong per-parameter text (especially team_id's API-key-vs-OAuth rule and integration_slug's server-side resolution), so the schema carries the load. The tool description adds only light context — headers as 'authentication headers' and the URL as the probe target — and never explains the two mutually exclusive invocation modes (server_url+headers vs integration_slug), which the agent must infer from the schema enum.

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-resource pair — 'Probe an MCP server URL and list the tools it exposes' — stating the exact action and outcome. It also names the sibling checkMcpOAuth and the OAuth-support angle that distinguishes it, so an agent can separate the tools 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.

Usage Guidelines5/5

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

The description gives an explicit trigger condition: 'Useful as a dry-run before creating a connection,' and spells out what it verifies (reachability, auth headers, tool catalog). It also names the alternative path — the OAuth check endpoint — and what that one probes for, providing clear when/when-not guidance.

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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