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AI Receptionist Callback Packet

ai_receptionist_callback_packet
Read-onlyIdempotent

Return a human-review callback packet for one AI Receptionist call. The packet is review-only and does not send SMS, place calls, or reply to customers autonomously.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
call_idYes
tenant_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
toolYes
public_nameNo
live_line_touchedNo
no_autonomous_outboundNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context by clarifying the packet is 'review-only' and explicitly listing external actions it does not perform, which helps the agent understand the tool's scope beyond the annotations.

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 a single focused sentence that front-loads the core purpose and then adds the key behavioral boundary. Every phrase earns its place, with no redundant or filler content.

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?

Given the simple parameter set, rich annotations, and presence of an output schema, the description is largely sufficient. The only notable gap is the lack of any explanation for tenant_id and the absence of routing guidance to the other AI Receptionist siblings, but the core call is well covered.

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%, so the description needed to compensate for the undocumented parameters. It mentions 'one AI Receptionist call', which loosely maps to call_id, but it provides no detail on tenant_id or the expected format/meaning of either parameter. The parameter names are somewhat self-explanatory, but the description does not add meaningful guidance beyond the 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 uses a specific verb and resource: 'Return a human-review callback packet for one AI Receptionist call.' It clearly scopes the tool to a single call and distinguishes it from sibling tools like ai_receptionist_review_queue and ai_receptionist_status by emphasizing the packet output rather than queue or status behavior.

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 implies usage: use this when you need a review-only callback packet for a specific AI Receptionist call. It explicitly states what the tool does not do ('does not send SMS, place calls, or reply to customers autonomously'), but it does not name alternatives or state when another AI Receptionist tool should be used instead.

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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TDQS

C2.6/5.0
Disambiguation1/5

Several tools are exact duplicates (talent_scout_my_profile_status and talent_scout_profile_status have identical descriptions), and eight estimator_estimate_* tools share the same generic description with no differentiation. This will cause misselection.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern, but there are inconsistencies: the duplicate profile tools have different naming (my_profile vs profile), and `fetch`/`search` are single-word verbs. Predictability is hampered by these deviations.

Tool Count2/5

65 tools is excessive for a coherent set, especially with many tools covering overlapping actions across multiple unrelated domains (AI receptionist, estimator, talent scout, GrowthOS). The count could be trimmed significantly.

Completeness3/5

The tool surface is broad and covers many lifecycle operations (create, read, export, record), but the duplicate tools and identical descriptions for estimator operations make it unclear whether all needed operations are present. Some expected operations like delete/update are missing for certain resources.

Resources