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Larkella

One industry in detail

industry
Read-onlyIdempotent

What Larkella's AI receptionist does for one kind of business, with an example call and that industry's common questions.

Input Schema

NameRequiredDescriptionDefault
industryYesWhich industry.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description does add content-level behavior — an example call and the industry's common questions are returned — which is useful, but it says nothing about depth, length, or format of the returned material.

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?

A single front-loaded sentence that names the subject first and then the contents. No waste, though it is thin enough that a clause about the alternative tool could have been added without bloat.

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 no output schema, the description must convey what comes back; it gestures at this (example call, common questions) but not the shape, depth, or whether the content is marketing copy versus structured data. Adequate for a simple one-param read tool, but leaves real ambiguity.

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 coverage is 100% and the single parameter has a five-value enum, so the schema carries the semantics. The description only restates 'one kind of business' without adding guidance about which vertical to pick or what happens for an unsupported one, so baseline 3 applies.

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 states a specific resource and scope: Larkella's AI receptionist offering for one kind of business, including an example call and common questions. This implicitly distinguishes it from the sibling 'industries' (detail for one vs. presumably a list), but the tool name 'industry' alone is vague and the differentiation is never made explicit.

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?

Usage is only implied: fetch this when you need in-depth content for a single vertical. There is no explicit when-to-use vs. 'industries', 'faq', or 'about', nor any statement of prerequisites or when this is the wrong choice.

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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