receptionist-toolkit

Should this business hire a receptionist?

should_i_hire_a_receptionist

Scores a business's phone coverage and returns a verdict — you're covered, get an AI front desk, or go hybrid — with the caller archetype, yearly leak, suggested plan, break-even days, and ROI.

Input Schema

TableJSON Schema

Name	Required	Description
`avgJobValue`	Yes	Average value of one new customer or job, USD (snapped to the quiz's brackets).
`callsPerWeek`	Yes	Roughly how many inbound calls per week (snapped to the quiz's brackets).
`currentSetup`	No	Who answers today: the owner (self), voicemail (vm), staff between tasks (staff), or nobody consistently (none). Default: self.
`missedRatePct`	Yes	Rough percent of calls that go unanswered (snapped to the quiz's brackets).
`coverageNeeded`	No	When calls actually come in. Default: business-hours.
`spanishCallers`	No	How often Spanish-speaking customers call. Default: no.

Output Schema

TableJSON Schema

Name	Required	Description
`cta`	No	One-line invite to try Lobby, with a signup link.
`score`	Yes	Phone-coverage maturity score, 0-100.
`verdict`	Yes	covered = current setup is fine; lobby = an AI front desk pays for itself; hybrid = AI + existing staff.
`recovery`	No
`archetype`	Yes
`yearlyLeak`	Yes	USD lost per year with the current setup.

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries the full transparency burden. It states the tool scores and returns a verdict with specific outputs, which implies a calculation/query with no side effects. However, it lacks details on data sources, model assumptions, or any limitations. It is adequate but not rich.

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, 25-word sentence that efficiently conveys the tool's purpose and outputs. It is front-loaded with the main action ('Scores... returns a verdict'). Could benefit from slight structuring (e.g., listing outputs) but overall very concise.

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?

The tool has 6 parameters (3 enums) and an output schema. The description covers the output categories but does not explain the meaning of output fields like 'caller archetype' or 'yearly leak'. Since an output schema exists, the description is minimally adequate, but additional context on the verdict logic would help.

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%, so the baseline is 3. The description does not add any parameter-level details beyond what the schema already provides. No additional semantics are introduced.

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 clearly states the tool scores phone coverage and returns a specific verdict with multiple outputs, distinguishing it from sibling tools like 'calculate_missed_call_cost' or 'simulate_receptionist_call'. The verb 'scores' and the resource 'business's phone coverage' are specific.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs alternatives. The description implies it's for deciding on receptionist hiring, but does not mention when not to use it or reference sibling tools for complementary tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All receptionist-toolkit tools Inspect

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct function: cost calculation, prompt generation, demo number retrieval, saving results, hiring decision, call simulation, IVR script writing, and general phone script writing. No overlapping purposes or ambiguous boundaries.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., calculate_missed_call_cost, write_ivr_menu). The naming is uniform and predictable across all 8 tools.

Tool Count5/5

With 8 tools, the toolkit covers the essential workflows for an AI receptionist system without being bloated or insufficient. The count is well-scoped for the domain.

Completeness5/5

The tool set covers the full lifecycle: analysis (calculate_missed_call_cost, should_i_hire_a_receptionist), creation (write_phone_script, write_ivr_menu, generate_elevenlabs_agent_prompt), simulation (simulate_receptionist_call), access (get_demo_call_number), and saving/sharing (save_my_receptionist). No obvious gaps.

Resources

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