Duvo MCP

Create Slack Channel Trigger

createAgentSlackTrigger

Create a Slack channel trigger on an agent (Agent in the Duvo UI): the agent starts a Run whenever a matching message is posted in the channel. An agent can carry one trigger per channel, so call this once per channel. The Slack workspace must be installed for the team AND bound to the agent's live build first (see the Slack bound-workspaces endpoint) — otherwise this returns 400.

Input Schema

TableJSON Schema

Name	Required	Description
`agent_id`	Yes	The agent's unique identifier
`channel_id`	Yes	Slack channel ID to watch, e.g. `C0123ABCD`.
`is_private`	No	Whether the channel is private. Defaults to false. Private channels require the Duvo Slack app to be invited to the channel.
`match_rule`	Yes	Which channel messages fire the trigger: `{"kind":"all"}` for every message, or `{"kind":"contains","values":["invoice"]}` to match keywords.
`channel_name`	Yes	Slack channel name shown in Duvo, without the leading `#`, e.g. `support-inbox`.
`trigger_type`	Yes	Trigger type. Only `slack_channel_message` (a message posted in a Slack channel) is supported here.
`slack_team_id`	No	Slack workspace (team) ID the channel belongs to, e.g. `T0123ABCD`. Defaults to the team's default installed workspace. Discover the workspaces an agent can use with the bound-workspaces endpoint.
`integration_instance_id`	No	Specific bound Slack connection to run the trigger off. Omit to use your own connection, or pass one returned by the bound-workspaces endpoint.

TDQS

A4.2/5.0

Behavior4/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description adds important behavioral details: the one-trigger-per-channel constraint, the requirement for workspace installation/binding, and the specific 400 error on missing prerequisites. No contradiction with annotations.

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?

The description is three sentences long, front-loaded with the core purpose, followed by a usage constraint and a prerequisite/failure mode. Every sentence carries meaningful information with no redundancy.

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?

Given the tool's complexity (8 params, no output schema), the description covers purpose, key constraints, prerequisites, and failure conditions. It doesn't explain success return values, but that's not required given rich schema annotations. Overall it provides enough context for correct invocation.

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 has 100% parameter description coverage, with detailed explanations for all 8 params including examples, defaults, and the match_rule oneOf structure. The description itself adds no parameter-level detail, but with full schema coverage, the baseline of 3 is appropriate.

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 clearly states the tool's function: 'Create a Slack channel trigger on an agent... the agent starts a Run whenever a matching message is posted in the channel.' It specifies the verb (create), resource (Slack channel trigger on an agent), and outcome (starts a Run). This distinguishes it from sibling tools like createAgentCaseTrigger, which handles case triggers instead.

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 provides clear usage context: 'An agent can carry one trigger per channel, so call this once per channel.' It also states prerequisites and a failure condition: 'The Slack workspace must be installed for the team AND bound to the agent's live build first... otherwise this returns 400.' While it doesn't explicitly name alternative tools for other trigger types, the domain is unambiguous.

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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