get_demo_call_numberReturns a real phone number anyone can call right now to talk to Lobby's AI receptionist live — plus suggested things to say (English and Spanish) and what to listen for (the mid-call language switch, booking flow).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| listenFor | No | ||
| trySaying | No | ||
| phoneNumber | Yes | ||
| availability | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes output but does not disclose behavioral traits like authentication, rate limits, or side effects. Since no annotations, description carries full burden but is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence conveying all essential information without waste. Well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and presence of output schema, description covers all necessary aspects for a simple retrieval tool. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, schema coverage 100%, description adds value by detailing what the return includes (phone number, scripts). Baseline 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool returns a live demo phone number plus suggested scripts and listening points. Verb 'returns' is specific and differentiates from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for obtaining a demo call number but does not explicitly state when to use versus alternatives or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.