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chernistry

Bernstein - Multi-agent orchestration

by chernistry
OverviewSchemaRelated ServersScoreDiscussions
Python
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load_skill

Fetch detailed skill documentation and resources for multi-agent orchestration, enabling agents to access full implementation details when needed for task execution.

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
referenceNo
scriptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: that agents only receive compact skill indexes initially, that this tool fetches detailed content, that scripts are returned as text rather than executed, and what the return structure contains. It doesn't mention potential errors, rate limits, or authentication requirements, but provides substantial operational context.

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 well-structured and efficiently organized. It begins with the core purpose, provides usage context, then details parameters with examples, and concludes with return information. Every sentence serves a clear purpose without redundancy. The formatting with clear sections (Args, Returns) enhances readability while maintaining brevity.

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?

Given the tool's moderate complexity (3 parameters, no annotations, but with output schema), the description provides complete context. It explains the tool's role in the agent workflow, parameter usage with examples, behavioral constraints (scripts not executed), and return structure. The existence of an output schema means the description doesn't need to detail return values, and it appropriately focuses on operational guidance.

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?

With 0% schema description coverage, the description fully compensates by providing detailed semantic information for all three parameters. It explains that 'name' matches index entries with examples, 'reference' points to files under references/ with examples, and 'script' points to files under scripts/ with examples and clarifies they're returned as text. The description adds significant value beyond the bare schema.

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 purpose: 'Load a skill pack body (and optionally a reference or script).' It specifies the exact resource (skill pack body, reference file, or script) and the action (fetch/load). The description distinguishes this tool from its siblings by explaining its unique role in fetching detailed skill content that agents only have in compact index form.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: '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 explains when to use the optional parameters (reference for deeper-context files, script for executable helper content) and clarifies what the tool does NOT do (the MCP harness does not execute scripts).

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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