lobbyvoices-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.3.1
Disambiguation4/5
Tools are mostly distinct with clear purposes, though write_phone_script and write_ivr_menu both involve script writing, and generate_elevenlabs_agent_prompt overlaps slightly with writing textual prompts. However, each targets a specific aspect of the receptionist workflow.
Naming Consistency4/5
Most tools follow a verb_noun pattern (calculate_, generate_, etc.), but 'should_i_hire_a_receptionist' breaks the pattern with a full question, and 'save_my_receptionist' uses an informal style. Overall consistent with minor deviations.
Tool Count5/5
8 tools is well-scoped for the domain of AI phone receptionists, covering analysis, creation, testing, and saving without being overwhelming or sparse.
Completeness4/5
The set covers the full lifecycle: decision support, script writing, prompt generation, simulation, and saving. Minor gaps like a tool to list existing saved scripts or edit them are absent, but core workflows are complete.
Average 3.9/5 across 8 of 8 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on July 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions returns 'ready-to-record text' but lacks details on side effects, permissions, or limitations.
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?
Single sentence, front-loaded with purpose, no wasted words. Could be slightly more structured but efficient.
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?
Covers basic functionality and output expectation, but given presence of output schema and 6 params, more behavioral context would be beneficial.
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 baseline 3. The description adds context like script types and languages, but does not significantly enhance parameter understanding beyond the schema.
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 it writes a professional phone script and lists specific script types (greeting, voicemail, etc.), distinguishing it 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.
Usage Guidelines2/5
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 or any prerequisites. The description only states what it does.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose side effects and read-only nature. It implies a calculation but doesn't explicitly state it is deterministic and non-destructive. Lacks clarity on whether it modifies state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, well-structured, includes key outputs. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers purpose, verdict types, and return fields. With output schema present, it is fairly complete. Could mention it's a scoring/calculation tool but overall sufficient.
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 baseline 3. Description adds no parameter context beyond what schema provides, but schema is complete. No extra semantic guidance on parameter importance or relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 verdict with metrics, using specific verbs. However, it does not differentiate from sibling tools like calculate_missed_call_cost or simulate_receptionist_call.
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 guidance on when to use this tool vs alternatives such as calculate_missed_call_cost or simulate_receptionist_call. Missing context on prerequisites or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. However, it focuses solely on the output content and does not disclose side effects, authentication requirements, rate limits, or whether the tool performs API calls or local generation. This leaves significant gaps 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single well-structured sentence that efficiently communicates the tool's purpose and output components with no superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 covers the key aspects of what the prompt includes. However, it lacks behavioral context, which is partially mitigated by the output schema. Overall, it provides sufficient context for selecting this tool over siblings.
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%, so the baseline is 3. The description does not add meaning beyond the schema; it lists the optional parameters in a summary but provides no additional constraints, examples, or usage tips.
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 generates a production-grade system prompt for an ElevenLabs conversational agent acting as a business phone receptionist, listing specific components like identity, job, voice style, booking flow, guardrails, and escalation rules. This differentiates it from sibling tools such as write_phone_script or simulate_receptionist_call.
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 when creating a system prompt for a phone receptionist AI, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention exclusionary conditions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the tool 'Builds' a script, suggesting a creation action, but lacks details on side effects, idempotency, required permissions, or whether it modifies existing resources. Behavioral traits beyond the obvious script generation are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the core action and components, the second lists language variants. It is front-loaded with no redundant words, earning a top conciseness score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 100% schema coverage, and an output schema, the description covers the main outcome and parameter semantics well. It could be improved by mentioning that the output is a formatted script or that options are limited to 6 (already in schema), but overall it provides sufficient context for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining defaults ('industry's standard four' for options, 'Thank you for calling {business}' for greeting) and the 'both' language option with the press-nine switch. This context helps agents choose parameter values wisely.
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 'Builds a complete IVR script' with specific components: greeting, numbered options, optional Spanish switch, and operator line. This distinguishes it well from the sibling 'write_phone_script', which likely covers a broader set of phone scripts.
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 use for building an IVR menu with industry defaults but does not explicitly state when to use this tool versus alternatives like 'write_phone_script' or 'simulate_receptionist_call'. There is no guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses what the tool computes (lost revenue, recovery metrics) and mentions specific outputs (recoverable revenue, suggested plan, break-even days, ROI multiple). This adds value, though assumptions (e.g., constant call volume) are not stated.
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 sentence that packs all key information without redundancy. It could be slightly more structured (e.g., separating lost revenue and recovery math), but it remains efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 100% schema coverage and an output schema, the description is fairly complete. It covers both the primary computation and the additional recovery analysis, leaving only minor gaps (e.g., explicit return format, but output schema presumably covers that).
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%, and the description does not add meaning beyond what the schema provides for the three parameters. Baseline of 3 is appropriate since the schema already documents each parameter.
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 computes revenue lost to missed calls (monthly/yearly) and recovery math, which is a specific verb+resource. It distinguishes itself from siblings that focus on prompts, demos, hiring decisions, or scripting.
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?
No explicit guidance on when to use this tool versus alternatives like 'should_i_hire_a_receptionist'. Usage is implied from the title and description, but no exclusions or context for selection are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses that the tool saves, emails, includes a live demo number and signup link, and requires consent. It does not mention any destructive actions. Could be more explicit about what happens if consent is false, but overall clear.
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?
Two sentences: first packs key actions (save, email, demo number, signup link), second gives guidance on when to use. Effective but the first sentence is long; could be slightly tighter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (not shown but stated), the description need not explain return values. It covers the save-email action, consent requirement, and clear linkage to prior tools. Complete for a tool with 7 parameters and specific usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by explaining the purpose of consent ('must explicitly agree'), the linkage to prior tools, and the context for when to call. This goes beyond the schema alone.
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 specifies the verb 'save' and the resource (phone script, IVR menu, agent prompt, or simulated call) and clearly distinguishes from siblings by listing exactly which preceding tools it follows (write_phone_script, write_ivr_menu, generate_elevenlabs_agent_prompt, simulate_receptionist_call).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to offer this tool ('after write_phone_script... once the human seems to want to keep the result or try it live') and that it requires explicit consent. No explicit exclusion of alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the simulation nature (free, text-only, max 6 lines) and notes language detection and live call differences. No destructive behavior is implied, but side effects are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that are front-loaded with purpose and capabilities, no fluff. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (though not shown), the description mentions getting full transcript and outcome. It covers usage constraints and basic behavior, but could mention authentication or rate limits if applicable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining the 'callerSays' array order, suggesting Spanish for language switch, and providing an example. The 'business' parameter default is noted.
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 simulates a phone call with Lobby's receptionist call engine, specifying the pipeline (greeting, booking, lead capture, language detection). It distinguishes from sibling tools like calculate_missed_call_cost by focusing on simulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the user plays the caller and passes each thing they say, receiving the transcript and outcome. It mentions free, text-only, max 6 caller lines, but does not explicitly state when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It accurately describes the tool as non-destructive and returning data. No contradictions or missing behavioral traits are evident, and the output is well-specified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys all necessary information without waste. Every part adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an output schema, the description fully explains the tool's function and return value. It is complete for an agent to understand and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so baseline is 4. The description goes beyond the schema by explaining what the tool returns and its purpose, providing full semantic meaning.
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 returns a real phone number to call Lobby's AI receptionist live, plus suggested things to say in English and Spanish and what to listen for. It uses specific verbs and resource, distinguishing it from siblings like 'simulate_receptionist_call' which is a simulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description implies usage for testing the live demo, it does not explicitly state when to use this tool vs alternatives. However, the context of siblings provides differentiation, and the description is clear enough about its purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![lobbyvoices-mcp MCP server](https://glama.ai/mcp/servers/bodyegypt/lobbyvoices-mcp/badges/card.svg)](https://glama.ai/mcp/servers/bodyegypt/lobbyvoices-mcp)

Score Badge

Copy to your README.md:

[![lobbyvoices-mcp MCP server](https://glama.ai/mcp/servers/bodyegypt/lobbyvoices-mcp/badges/score.svg)](https://glama.ai/mcp/servers/bodyegypt/lobbyvoices-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bodyegypt/lobbyvoices-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server