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Duvo MCP

by io.github.duvoai

Create Skill

createSkill

Create or update a skill from a JSON body. The server constructs SKILL.md from the provided fields and stores it. If a skill with the same name already exists in the team, it is updated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesSkill name. 1-64 lowercase alphanumeric chars and hyphens; no leading/trailing or consecutive hyphens.
contentYesMarkdown body of SKILL.md without YAML frontmatter. The server prepends the frontmatter from the other fields.
licenseNoOptional: license name or reference to a bundled license file.
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.
metadataNoOptional: arbitrary key-value metadata.
descriptionYes1-1024 chars describing what the skill does and when to invoke it.
allowed-toolsNoOptional (experimental): space-delimited list of pre-approved tools.
compatibilityNoOptional: 1-500 chars describing environment requirements.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already signal that this is a non-read-only, non-idempotent operation. The description adds meaningful behavioral detail beyond annotations: the server constructs a SKILL.md file from the input fields and stores it, and existing skills with the same name are updated. This clarifies the storage mechanism and overwrite behavior, which annotations do not convey. 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 two concise, front-loaded sentences. It states the core behavior, the storage mechanism, and the upsert semantics without any wasted words. This is appropriately sized for a tool with 8 parameters because the schema carries the detailed documentation.

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?

The description provides a clear high-level overview of the operation and its upsert behavior, which is sufficient given the thorough schema and the presence of sibling revision tools. Minor gaps remain: it does not describe the response format (no output schema exists) or explicitly address how this tool relates to revisions, but these are not critical for selecting and invoking the tool correctly.

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 provides thorough descriptions for all 8 parameters, including constraints on name, description, and team_id. With 100% schema description coverage, the description's generic reference to a 'JSON body' does not add parameter-level meaning beyond the schema. The baseline of 3 is appropriate because the schema handles the documentation 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: 'Create or update a skill from a JSON body.' It clearly distinguishes the tool from createSkillRevision by describing the skill-level upsert and SKILL.md construction. The update semantics are explicitly stated ('If a skill with the same name already exists in the team, it is updated').

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 implies the primary use case: creating new skills or updating existing ones by name. It also provides the key contextual behavior of overwriting/updating an existing skill with the same name. However, it does not explicitly discuss alternatives such as createSkillRevision or updateSkillFile, so there is no clear 'when not to use' guidance. The context is clear but exclusions are absent.

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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