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Set up an AI receptionist

upsert_receptionist

Creates or edits an AI receptionist: name, greeting, and the knowledge it tells people who dial in. Business plan. It speaks with the caller and does not transfer the live conversation to a person.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
nameNo
voiceNo
enabledNo
greetingNo
knowledgeNo
languagesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare this is a write (readOnlyHint=false), non-destructive, non-idempotent, closed-world operation, so the safety profile is covered. The description adds two useful facts beyond that: the plan gating requirement and the no-live-transfer interaction behavior. It still does not say what editing overwrites or how id/idempotency behaves, so it stays at 3.

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?

Two compact sentences, front-loaded with the action and configured fields. The 'Business plan.' fragment is terse but readable, and nothing is padded.

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

Completeness2/5

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

For a 7-parameter mutation with 0% schema coverage and no output schema, the description is too thin: it omits half the parameters (voice, enabled, languages, id) and gives no hint of what the edit overwrites or returns. An agent cannot reliably invoke this without guessing at parameter meaning.

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

Parameters2/5

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

Schema description coverage is 0% across 7 parameters, so the schema documents nothing. The description names only three fields (name, greeting, knowledge) and leaves voice, enabled, languages, and especially id undocumented, so it does not compensate for the coverage gap.

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?

States a clear verb pair (creates or edits) and resource (AI receptionist), and enumerates what it configures: name, greeting, and knowledge. It is not differentiated from sibling upserts (upsert_hours, upsert_menu, upsert_queue), which an agent must disambiguate by resource name alone.

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 'Business plan' fragment implies a plan prerequisite, and 'does not transfer the live conversation to a person' hints at scope. However, there is no explicit when-to-use versus siblings like control_call or upsert_queue, nor any statement of when this should not be used.

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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