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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.

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

The description discloses the key non-obvious side-effect: if a skill with the same name already exists in the team, it is updated, which implies possible overwriting of existing content. It also clarifies that the server generates SKILL.md from the provided fields, adding behavioral context beyond the 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 sentences, front-loads the core action, and includes the important update behavior without filler. Every clause contributes useful information.

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?

The description covers the essential create/update behavior and team-scoped name uniqueness, while the schema handles parameter documentation. However, with no output schema, the description does not say what the call returns, and it gives no indication of how this tool relates to the revision workflow represented by createSkillRevision and promoteSkillRevision.

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%, and the schema already documents name constraints, content behavior, team_id fallback rules, and optional fields. The description adds only the high-level 'JSON body' framing and SKILL.md construction, which is helpful but does not meaningfully expand parameter-level understanding.

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

Purpose4/5

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

The description uses a specific verb-resource pair ('Create or update a skill') and adds concrete behavior: the server constructs SKILL.md and stores it. It is clear, but it does not explicitly differentiate the tool from closely related siblings like createSkillRevision or updateSkillRevision, so it stops short of a 5.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance is provided, and the description never mentions alternatives such as createSkillRevision, updateSkillRevision, or updateSkillFile. The only usage hint is the conditional update-on-same-name behavior, which describes behavior rather than telling the agent when to select this tool over its siblings.

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.

Resources