receptionist-toolkit

Generate an ElevenLabs agent system prompt

generate_elevenlabs_agent_prompt

Generates a production-grade system prompt for an ElevenLabs conversational agent acting as a business phone receptionist: identity, job, voice style, booking flow, guardrails, and escalation rules.

Input Schema

TableJSON Schema

Name	Required	Description
`biz`	Yes	Business name (required).
`tone`	No	Personality, e.g. warm, formal, upbeat.
`hours`	No	Business hours in plain words.
`tasks`	No	What the agent should do, e.g. book, faqs, leads.
`spanish`	No	Whether the agent should also handle Spanish callers.
`industry`	No	Industry, e.g. plumbing, hvac, dental, salon, law, restaurant.
`agentName`	No	Name the agent should use for itself.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes	The complete system prompt, ready to paste into ElevenLabs.
`sections`	No	The prompt broken into tagged sections.

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It only states the output type (system prompt) without mentioning any side effects, limitations, generation time, or prerequisites. This is insufficient for an agent to fully understand the tool's behavior.

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, focused sentence that is concise and front-loaded with the core action. It avoids unnecessary words, but could be slightly more structured without adding length.

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?

Given the tool has 7 parameters and an output schema, the description is adequate but does not elaborate on return values or expected output format beyond 'system prompt'. For a generative tool, a bit more context about output usage would be helpful.

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% with basic descriptions for all 7 parameters. The tool description adds some context (e.g., 'booking flow' hinting at tasks) but does not significantly enhance meaning beyond what the schema already provides. Baseline score of 3 is appropriate.

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's purpose: generating a production-grade system prompt for an ElevenLabs conversational agent acting as a business phone receptionist. It specifies the content areas (identity, job, voice style, etc.), distinguishing it from sibling tools like calculate_missed_call_cost or write_phone_script.

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 for creating agent prompts but provides no explicit guidance on when to use this tool versus alternatives like write_phone_script. It lacks exclusion criteria or context for specific scenarios.

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

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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