agent4.io

create_skill

Create a skill. description = "when to use" (goes into the system prompt, keep it short); instructions = the detailed guide (fetched on demand via load_skill).

The paradigm (important): instructions are visible only AFTER the model calls load_skill — if
calling some tool is **mandatory** ("user gives a phone number → must call save_contact"), the
trigger must be written into description, otherwise models often answer directly without
loading the skill and the rule never takes effect. Also never promise return values a tool
doesn't produce (ticket numbers / IDs) — the model will fabricate them. The returned warnings
field flags both patterns.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`tools`	No
`description`	No
`instructions`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 28, 2026

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It explicitly reveals that instructions are only visible after load_skill is called, warns about model fabrication of return values, and mentions the returned warnings field. This goes well beyond the basic create operation and discloses critical behavioral nuances.

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

Conciseness4/5

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

The description is front-loaded with 'Create a skill' and then provides essential field guidance and paradigm explanation. While the paradigm paragraph is dense, every sentence adds information about critical pitfalls and system behavior. It is appropriately structured despite being longer than average.

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 covers the core operational paradigm, the roles of the main fields, and warns about common mistakes. However, it omits any explanation of the 'tools' parameter, which is a significant gap given the absence of annotations and parameter descriptions in the schema. Since an output schema exists, not detailing return values is acceptable.

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 description clearly explains the meaning of the 'description' and 'instructions' parameters, but does not address the 'tools' parameter, which is not self-evident. Since schema description coverage is 0%, the description only partially compensates for the missing parameter information, leaving a notable gap.

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 'Create a skill' with a specific verb and resource. It further elaborates on the two key fields (description and instructions), making the tool's purpose unambiguous and distinct from sibling create tools like create_agent or create_knowledge_base.

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 substantial context on how to use the tool effectively: what to put in description vs instructions, the importance of triggers in the description, and avoiding promises of return values. While it doesn't explicitly contrast with update_skill or other alternatives, the guidance is clear and directly applicable to the tool's usage.

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

All agent4.io tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?