Free receptionist tools: phone scripts, IVR menus (EN+ES), ElevenLabs prompts, missed-call math
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. |
| Name | Required | Description |
|---|---|---|
| cta | No | One-line invite to try Lobby, with a signup link. |
| inputs | No | |
| yearly | Yes | |
| monthly | Yes | |
| recovery | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly indicates a pure calculation (no side effects) by listing computed outputs. However, it could explicitly state it is read-only and does not modify any data.
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 captures the tool's purpose and key outputs. It is front-loaded with the primary action ('computes the revenue...') with no unnecessary words.
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 simple input schema (3 numeric parameters) and the presence of an output schema (so return values are documented), the description sufficiently covers what the tool does and what it returns. It lists all major output categories.
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 schema already documents all three parameters. The tool description does not add additional meaning or context beyond what's in the 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 it computes revenue loss and recovery math, using specific verbs like 'computes' and listing concrete outputs (monthly/yearly loss, recoverable revenue, etc.). It distinguishes from sibling tools that are about creating prompts, getting numbers, or simulating calls.
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 usage by stating what it computes, but does not explicitly say when to use this tool versus alternatives like 'should_i_hire_a_receptionist' or 'simulate_receptionist_call'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
| Name | Required | Description |
|---|---|---|
| prompt | Yes | The complete system prompt, ready to paste into ElevenLabs. |
| sections | No | The prompt broken into tagged sections. |
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.
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.
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.
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.
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.
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.
Returns 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 parameters | |||
| Name | Required | Description |
|---|---|---|
| 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.
Saves the phone script, IVR menu, agent prompt, or simulated call you just built for a real business and returns the live demo number plus signup link in this conversation. It does not send email. Offer this only after the human explicitly agrees to save their details, and only call it with consent: true.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email identity for the consented saved record (required; no email is sent). | ||
| notes | No | Any other context worth keeping — what the business does, what was discussed. | |
| consent | Yes | Required. The person must explicitly agree to save their details. If false or omitted, do not save anything. | |
| industry | No | Industry or trade, e.g. plumbing, dental, salon. | |
| language | No | Language for the saved setup. Default: en. | |
| phone_script | No | The phone script, IVR menu text, or agent prompt to save, if one was generated earlier in this conversation. | |
| business_name | Yes | The business name (required). |
| Name | Required | Description |
|---|---|---|
| saved | Yes | |
| business | No | |
| emailSent | No | |
| signupUrl | No | |
| demoNumber | No | |
| confirmation | Yes | Friendly confirmation to relay to the person, including the live demo number and signup link. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It reveals that the call persists data, returns a demo number and signup link, does not send email, and must be gated on explicit consent. It could go further on side effects like overwrites or data retention, but it covers the most decision-relevant behaviors.
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 three tight sentences with no filler. It leads with the core action and outputs, then states the negative ('does not send email'), then gives the consent gate. Every sentence earns its place.
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 captures what is saved, what is returned, the consent requirement, and the no-email behavior. Combined with a fully described schema and an output schema, an agent has everything needed to decide whether and how to call this tool correctly.
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 schema already explains every parameter. The description adds only a behavioral emphasis on consent and the 'no email' clarification, which doesn't materially extend the 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 names a specific verb ('Saves') with a clear resource ('the phone script, IVR menu, agent prompt, or simulated call you just built for a real business') and explicitly states the outputs ('live demo number plus signup link'). This clearly distinguishes it from the sibling generation and simulation tools: it is the persistence step, not the creation step.
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 gives a strong usage condition: 'Offer this only after the human explicitly agrees to save their details, and only call it with consent: true.' It also clarifies a relevant exclusion ('It does not send email'). However, it does not explicitly compare this tool to sibling alternatives, so the routing guidance is contextful but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
| 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. |
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.
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.
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.
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.
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.
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.
Role-play a phone call with Lobby's receptionist call engine — the same pipeline behind the product demo: greeting, booking flow, lead capture, and automatic English/Spanish detection (live calls add a full AI brain on top). You play the caller: pass each thing the caller says, get the full transcript and outcome back. Free, text-only, max 6 caller lines.
| Name | Required | Description | Default |
|---|---|---|---|
| business | No | Business name the receptionist answers for. Default: Lobby Demo Services. | |
| callerSays | Yes | The caller's lines, in order. Try Spanish to hear the language switch — e.g. ['Hola, necesito una cita para mañana.'] |
| Name | Required | Description |
|---|---|---|
| booked | No | |
| outcome | Yes | |
| language | Yes | Language the receptionist detected and answered in. |
| hearItLive | No | Phone number to call to experience the same receptionist with a real voice. |
| transcript | Yes | |
| leadCaptured | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the pipeline stages, language detection capability, and the fact that it's the same pipeline as the demo (but without the 'full AI brain'). It also notes it's free and text-only, which are key behavioral traits for an AI agent.
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 extremely concise: two sentences that effectively front-load the purpose and then provide usage instructions. Every sentence adds value with no redundancy or 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?
Despite moderate complexity, the description covers input, behavior, constraints, and hints at output. Since an output schema exists, the lack of explicit return value details is acceptable. The description is complete for an AI agent to understand and invoke the tool correctly.
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%, so the baseline is 3. The description adds value by providing an example (Spanish language switch) and explaining that callerSays is the caller's lines in order. This helps the agent understand how to format input beyond the schema's description.
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 purpose: role-playing a phone call with Lobby's receptionist call engine. It lists key features (greeting, booking, lead capture, language detection) and explicitly distinguishes from sibling tools like calculate_missed_call_cost, making its purpose unmistakable.
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 clear usage guidance: the user plays the caller, passes each line, and receives transcript and outcome. It specifies constraints (free, text-only, max 6 lines) and hints at language switching. While it doesn't explicitly contrast with siblings, the context makes it obvious when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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). |
| Name | Required | Description |
|---|---|---|
| cta | No | One-line invite to try Lobby, with a signup link. |
| scripts | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It accurately describes the tool as generating script text, but lacks details on permissions, side effects, or limits. The behavior is straightforward and non-destructive, so a 3 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?
The description is a single, well-structured sentence that front-loads the core action and output. Every piece of information is relevant, with no wasted words.
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 covers the essential aspects: what it writes, languages, and output format. With an output schema present and high schema coverage, it is complete enough for a generative tool, though it omits default values for mode and vibe.
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%, so the description adds limited extra meaning beyond indicating the script types and languages. The baseline of 3 applies; the description reinforces but does not significantly enhance parameter understanding.
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 writes phone scripts, specifies types (greeting, voicemail, hold, jingle) and languages (English, Mexican Spanish), and notes it returns ready-to-record text, distinguishing it from siblings 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?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or compare with sibling tools, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
save_my_receptionist3 fields changedInput schema / properties / consent / descriptionPrevious value: -"Required. The person must explicitly agree to have their saved result emailed to them, plus one follow-up. If false or omitted, do not save or email anything — refuse politely and explain you need their OK first."New value: +"Required. The person must explicitly agree to save their details. If false or omitted, do not save anything."Input schema / properties / email / descriptionPrevious value: -"Email to send the saved result and one follow-up to (required)."New value: +"Email identity for the consented saved record (required; no email is sent)."Input schema / properties / language / descriptionPrevious value: -"Language for the follow-up email. Default: en."New value: +"Language for the saved setup. Default: en."calculate_missed_call_cost1 field changedOutput schema / properties / ctaAdded value: +{
+ "description": "One-line invite to try Lobby, with a signup link.",
+ "type": "string"
+}should_i_hire_a_receptionist1 field changedOutput schema / properties / ctaAdded value: +{
+ "description": "One-line invite to try Lobby, with a signup link.",
+ "type": "string"
+}write_ivr_menu1 field changedOutput schema / properties / ctaAdded value: +{
+ "description": "One-line invite to try Lobby, with a signup link.",
+ "type": "string"
+}write_phone_script1 field changedOutput schema / properties / ctaAdded value: +{
+ "description": "One-line invite to try Lobby, with a signup link.",
+ "type": "string"
+}save_my_receptionistget_demo_call_numbersimulate_receptionist_callcalculate_missed_call_costgenerate_elevenlabs_agent_promptshould_i_hire_a_receptionistwrite_ivr_menuwrite_phone_scriptClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Verified pricing for 21 answering services & AI receptionists, dated weekly. Missed-call ROI calc.
AI phone answering tools for service businesses — ROI, missed calls, demos across 18 industries
40+ Lightning-paid AI tools for agents: calls, SMS, fax, voice, translation. No signup, no keys.
Give your AI agent a phone: place calls, navigate IVRs, wait on hold, get structured answers.
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.
