receptionist-toolkit
Server Details
Free receptionist tools: phone scripts, IVR menus (EN+ES), ElevenLabs prompts, missed-call math
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.5/5 across 5 of 5 tools scored.
Each tool addresses a distinct aspect of phone reception management: cost calculation, AI agent prompt generation, hiring decision assessment, IVR menu creation, and phone script writing. There is no functional overlap.
Most tools use a verb_noun pattern (calculate_, write_, generate_), but 'should_i_hire_a_receptionist' breaks the pattern with a question format. Naming is descriptive and readable, but not fully consistent.
Five tools is an ideal size for a focused domain like receptionist tools. It covers essential functions without being overwhelming or too sparse.
The toolkit covers key areas: cost analysis, hiring decision, agent prompts, IVR, and phone scripts. Missing integration or testing tools, but the core planning and content generation capabilities are complete for the intended purpose.
Available Tools
5 toolscalculate_missed_call_costCalculate what missed calls cost a businessAInspect
Computes the revenue a business loses to missed phone calls (monthly and yearly), plus the recovery math: recoverable revenue, suggested answering plan, break-even days, and ROI multiple.
| Name | Required | Description | Default |
|---|---|---|---|
| avgJobValue | Yes | Average value of one new customer or job, USD. | |
| callsPerWeek | Yes | Inbound calls per week. | |
| missedRatePct | Yes | Percent of calls missed or sent to voicemail. |
Output Schema
| Name | Required | Description |
|---|---|---|
| inputs | No | |
| yearly | Yes | |
| monthly | Yes | |
| recovery | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses it is a computation tool with no side effects, but does not detail any behavioral traits such as data requirements or response time. It is adequate for a simple calculator.
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?
The description is a single sentence that efficiently communicates the tool's purpose and outputs, with no unnecessary words or repetition.
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?
The description lists key outputs and the tool has a supporting output schema. For a straightforward calculator, it provides sufficient context. Minor gap: no mention of assumptions or data sources.
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?
Schema coverage is 100%; each parameter is already well-described in the schema. The description adds no additional meaning beyond the schema, thus meeting the baseline.
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?
The description clearly states what the tool computes ('revenue a business loses to missed phone calls') and lists specific outputs ('recoverable revenue, suggested answering plan, break-even days, and ROI multiple'). It distinguishes itself from sibling tools which focus on prompts, hiring decisions, or script writing.
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?
The description implies use for financial loss analysis but lacks explicit guidance on when to use versus alternatives. No when-not or direct sibling comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_elevenlabs_agent_promptGenerate an ElevenLabs agent system promptBInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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
| Name | Required | Description |
|---|---|---|
| prompt | Yes | The complete system prompt, ready to paste into ElevenLabs. |
| sections | No | The prompt broken into tagged sections. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any side effects, authentication needs, rate limits, or whether the operation is read-only or creates new data. The description only describes the output content, not behavioral traits.
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?
The description is a single, well-structured sentence that front-loads the main purpose. Every part is informative, with no wasted words. It is appropriately sized for the tool's complexity.
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 the tool has 7 parameters, no enums, and an output schema, the description provides a good overview but lacks usage guidelines and behavioral transparency. It mentions key components but does not fully compensate for missing annotations. Adequate but with 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?
Schema description coverage is 100%, so baseline is 3. The description adds overall context but does not provide additional meaning per parameter beyond the schema. For example, it does not elaborate on valid values for 'tasks' or 'tone'. No extra value above schema.
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?
The description clearly states the tool generates a system prompt for an ElevenLabs conversational agent acting as a phone receptionist. It lists specific components like identity, job, voice style, booking flow, guardrails, and escalation rules. This is a specific verb+resource+scope that distinguishes it from sibling tools like write_phone_script or calculate_missed_call_cost.
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?
No explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, nor does it compare with sibling tools. The description implies use for AI agent prompts but lacks explicit context or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
should_i_hire_a_receptionistShould this business hire a receptionist?BInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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
| Name | Required | Description |
|---|---|---|
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention that the tool is read-only, non-destructive, or any side effects (e.g., no data is modified). The agent has no information on safety or permissions from the description.
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?
The description is a single, dense sentence that efficiently communicates the tool's purpose and outputs. Every element (verdict, archetype, leak, plan, break-even, ROI) earns its place with no fluff.
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 the presence of an output schema (covering return values) and high schema coverage, the description adequately summarizes the tool's overall functionality. It could mention that the tool is analytical and non-destructive, but this gap is partially offset by the output schema and the nature of the tool.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds no new parameter insight beyond the schema; it only summarizes the tool's output. No extra semantics or usage tips for parameters are provided.
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?
The description clearly states the tool's verb ('scores') and resource ('business's phone coverage'), and itemizes the verdict and return fields (caller archetype, yearly leak, etc.). It distinguishes from sibling tools like calculate_missed_call_cost and write_phone_script by focusing on the decision between human, AI, or hybrid front desk.
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?
The description provides no explicit guidance on when to use this tool versus its siblings or when not to use it. It merely states what it does, leaving the agent to infer usage context from the name and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
write_phone_scriptWrite a business phone scriptAInspect
Writes a professional phone script for a business — greeting, voicemail message, on-hold message, or jingle lines — in English, Mexican Spanish, or both. Returns ready-to-record text.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | What kind of script to write. Default: greeting. | |
| vibe | No | Tone of voice. Default: warm. | |
| extra | No | Optional details to mention: hours, offers, callback promise. | |
| langs | No | Languages to write. Default: both. | |
| trade | No | Industry or trade, e.g. plumbing, dental clinic. | |
| business | Yes | Business name (required). |
Output Schema
| Name | Required | Description |
|---|---|---|
| scripts | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It states it 'writes' and returns 'ready-to-record text', implying non-destructive generation. However, it doesn't mention side effects, auth needs, or limitations beyond that.
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 with all key information: action, resource, types, languages, output. No wasted words, 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 the presence of an output schema (true), the description adequately explains the output format. It covers the main use cases and variants, making it complete for this generative tool.
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?
Schema covers 100% of parameters with descriptions. The tool description adds that output is 'ready-to-record text' and clarifies language options, but doesn't add significant detail beyond schema. Baseline 3 is appropriate.
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?
The description clearly states the verb 'writes' and the resource 'professional phone script', and specifies types (greeting, voicemail, hold, jingle) and language options (English, Mexican Spanish, or both). It distinguishes from sibling tools like write_ivr_menu.
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?
No guidance on when to use this tool vs alternatives. Siblings are listed but no context for selection. Lacks when-not-to-use or prerequisites.
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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