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sipyourdrink-ltd

Bernstein - Multi-agent orchestration

load_skill

Retrieve the full body of a skill pack when its compact index indicates relevance. Optionally fetch a reference file or script content to deep-dive into implementation details.

Instructions

Load a skill pack body (and optionally a reference or script).

Agents receive only a compact skill index in their system prompt. Call this tool to fetch the full SKILL.md body for a skill when you decide it's relevant to the current task. Pass reference to get a deeper-context file or script to read the content of an executable helper.

Args: name: Skill name (matches the index entry, e.g. "backend"). reference: Optional filename under references/ - for example "python-conventions.md". script: Optional filename under scripts/ - for example "lint.sh". The script content is returned as text; the MCP harness does not execute it.

Returns: JSON with name, body, available_references, available_scripts, and the optional fetched content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
scriptNo
referenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided. The description discloses the return structure (JSON with name, body, available_references, available_scripts, and optional fetched content) and notes that script content is returned as text and not executed. It implies a read-only operation with no side effects, which is sufficient for this fetch tool.

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 structured with a clear purpose sentence, usage context, and a bulleted list of arguments and returns. Every sentence adds information without redundancy. It is front-loaded with the main action.

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

Completeness5/5

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

The tool has 3 parameters (1 required) and an output schema. The description covers the input parameters, return fields, and the rationale for calling it. It is complete for a fetch operation; error handling is not detailed but acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries full burden. It explains that 'name' matches the index entry (e.g., 'backend'), 'reference' is an optional filename under 'references/', and 'script' is optional under 'scripts/'. Examples are given, adding meaning beyond the bare types.

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 states 'Load a skill pack body (and optionally a reference or script).' It explains that agents receive a compact index and call this to fetch the full SKILL.md body for a skill. The verb 'load' and resource 'skill body' are specific, and the usage context distinguishes it from the compact index, though siblings are unrelated.

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 explains when to use: 'Call this tool to fetch the full SKILL.md body for a skill when you decide it's relevant to the current task.' It also describes optional parameters for deeper context. It does not explicitly state when not to use or give alternatives, but the context is clear given siblings are different.

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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