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fareboxfun

farebox-mcp

by fareboxfun

call_skill

Generate a concise meta description for the call_skill tool from farebox-mcp, following all formatting constraints.

Instructions

Call a Farebox built-in skill (summarize, translate, code-review, explain, sentiment, extract-data). Faster than crafting a prompt from scratch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesThe text or content to process.
modelNoOverride the default model for this skill.
skillYesSkill ID.
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It does not mention any behavioral aspects: whether the tool is read-only, whether it incurs costs/usage, effects on model state, or how output is structured. For a tool that processes content through a model with potential cost implications, the lack of any behavioral disclosure is a significant gap.

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 a single efficient sentence that conveys purpose and a comparative benefit with minimal waste. It's appropriately front-loaded, stating the action first and examples after. Could arguably mention a caveat or additional context, but for its brevity it is well structured.

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?

With 3 params, full schema coverage, and no output schema, the description gives the essential purpose and a usage hint. It is adequate for agent selection. However, it does not explain what each skill produces, whether output formatting varies, or any cost considerations—information that would matter for an agent deciding which skill to invoke, especially since there is no output schema to fill these gaps.

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%, so parameters are fully documented structurally. The description enumerates example skill names that overlap with the enum but adds no meaning beyond the schema—it doesn't explain what 'input' expects in terms of format/length, or what the 'model' override does functionally. With full schema coverage, the baseline is 3, and the description does not exceed it.

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 ('Call a Farebox built-in skill') and lists example skills (summarize, translate, code-review, etc.), making the core purpose clear. It does not explicitly distinguish from sibling tools, though 'Farebox built-in skill' implies a narrower pre-defined scope versus the generic 'chat' sibling. The examples help but are somewhat redundant with the schema enum.

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

Usage Guidelines3/5

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

The description offers a comparative benefit ('Faster than crafting a prompt from scratch'), suggesting when users would prefer this over a chat/prompt approach. However, it does not explicitly state when NOT to use it or describe alternatives like chat for custom/unstructured needs. There is no guidance on when the generic chat sibling would be more appropriate than a fixed skill.

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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