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Create agent from notes

create_agent_from_notes

Create a new draft agent for a business without a website, from a few written facts: what it sells, prices, hours, location, delivery and how to order or book. Returns its agent_id, build status and dashboard URL. The build runs in the background for about a minute, and the website chat widget stays off.

Input Schema

NameRequiredDescriptionDefault
languageNoThe language the agent answers in first.en
business_nameNoThe business name, if the notes do not make it obvious.
business_notesYesPlain facts about the business in the owner's words: products or services with prices, opening hours, address or area served, delivery, payment, and how customers order or book. The agent answers only from these notes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false), yet the description adds genuinely non-obvious behavior: the build is asynchronous (~1 minute), the result is a draft with the website widget deliberately off, and the response carries agent_id, build status and dashboard URL. The one notable omission is that idempotentHint=false — repeat calls create duplicates — is never warned about.

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?

Three tight sentences: purpose and scope first, then the return payload, then the async/build-state caveat. Every sentence carries distinct information and nothing is padding.

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?

With no output schema, the description correctly shoulders the burden of explaining the return values and does so. It also covers the async lifecycle and widget-off default. Only the duplicate-creation consequence of non-idempotency is left out.

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 all three parameters (including the business_notes length bound and the language enum) are already documented. The description's list of facts (prices, hours, location, delivery, ordering) largely restates the business_notes schema text, adding reinforcement rather than new meaning; baseline 3 applies.

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?

States a specific verb and resource ("Create a new draft agent") and narrows the scope with "for a business without a website, from a few written facts," which cleanly separates it from the create_agent_from_website sibling without naming it. The parenthetical enumeration of what the notes should contain makes the input target unambiguous.

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 phrase "for a business without a website" gives a clear implicit selection rule against create_agent_from_website, and "draft agent" signals the follow-up path (set_website_widget, add_knowledge). It stops short of explicitly naming the alternative tool or stating prerequisites, so it is a strong context signal rather than a full when/when-not statement.

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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