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  3. Standard Vocal MCP
Standard Vocal MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 7:45:58 AM

Standard Vocal MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

Voice Agent Factory for Vapi phone agents: vertical templates, self-testing agents, audio forensics, prompt versioning, and CI regression gates. npx -y standard-vocal-mcp

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "guillaumehussong-standard-vocal-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "standard-vocal-mcp"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (6) Directory Badge Claim listing AlternativesπŸ—£οΈ More in Conversational AI

Capabilities & Tool Schemas (6) ~280 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server β€” may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Standard Vocal MCP.

list_agents

Lists the assistants on the account (id, name, model, voice, transcriber) β€” the entry point to find an `assistantId`.

deploy_agent

One tool = one full vertical deployment. `deploy_agent(vertical: elagueur\

run_eval

The agent tests itself.** Fetches the live system prompt, simulates N scripted scenarios against the configured LLM, scores behavior (close speed, no price quoting, spelled digit confirmation, urgency priority, solicitation refusal), returns a `/100` grade with PASS / WARN / FAIL. No audio, no bill…

audio_forensics

Downloads the 3 recording tracks of a call (mono / customer / assistant), runs RMS-per-window analysis, and **locates the noise source**. Automates the exact manual investigation that found a "computer noise" issue was injected downstream of the caller's mic, not in Vapi.

prompt_diff

Prompts as code. `snapshot` captures the live prompt into local history, `diff` compares two versions, `rollback` pushes an old prompt back to Vapi.

regression_gate

CI for prompts.** Runs `run_eval` and blocks the update if the score regressed vs the stored baseline. Returns allow/deny with the reason.

Documentation Overview

standard-vocal-mcp

A Voice Agent Factory exposed over the Model Context Protocol. Not a CRUD wrapper β€” a factory that deploys production phone agents, tests them, audits their audio, and gates their prompts like code.

Built on top of Vapi (telephony + LLM + STT + TTS). Differs from the official vapi-mcp-server, which is a generic CRUD wrapper over the Vapi API. This is the factory layer: vertical templates, self-testing agents, audio forensics, prompt versioning, CI regression gates.

Why it's hard to copy

The official Vapi MCP is 20 tools that each map to one raw API call. Anyone can read the docs and rebuild it in an hour.

This one encodes months of empirical tuning into its templates β€” transcriber parameters (numerals, confidenceThreshold, keywords, endpointing), voice selection, conversation flow, urgency handling β€” plus evaluation logic and audio diagnostics that require knowing the Vapi artifacts API and audio signal processing. The longer it runs, the more reference data it accumulates. That's the moat.

Tools

ToolWhat it does
list_agentsLists the assistants on the account (id, name, model, voice, transcriber) β€” the entry point to find an assistantId.
deploy_agentOne tool = one full vertical deployment. deploy_agent(vertical: elagueur|plombier, company, extraKeywords) creates a complete Vapi assistant: prompt, voice, transcriber, keywords, greeting.
run_evalThe agent tests itself. Fetches the live system prompt, simulates N scripted scenarios against the configured LLM, scores behavior (close speed, no price quoting, spelled digit confirmation, urgency priority, solicitation refusal), returns a /100 grade with PASS / WARN / FAIL. No audio, no billing β€” pure prompt evaluation. Global hard checks on every scenario: phone fidelity (any number the agent speaks must be one the caller gave β€” catches hallucinated numbers that plain "did it spell the digits?" checks miss) and anti-leak (template brackets never reach the caller's ear). One hard failure = verdict FAIL, whatever the grade.
audio_forensicsDownloads the 3 recording tracks of a call (mono / customer / assistant), runs RMS-per-window analysis, and locates the noise source. Automates the exact manual investigation that found a "computer noise" issue was injected downstream of the caller's mic, not in Vapi.
prompt_diffPrompts as code. snapshot captures the live prompt into local history, diff compares two versions, rollback pushes an old prompt back to Vapi.
regression_gateCI for prompts. Runs run_eval and blocks the update if the score regressed vs the stored baseline. Returns allow/deny with the reason.

Quickstart

server.ts
# Claude Code
claude mcp add standard-vocal -- npx -y standard-vocal-mcp

# Env
export VAPI_TOKEN="your_vapi_token"
export OPENAI_API_KEY="your_openai_key"   # used by run_eval to simulate scenarios
jsonc
// claude_desktop_config.json
{
  "mcpServers": {
    "standard-vocal": {
      "command": "npx",
      "args": ["-y", "standard-vocal-mcp"],
      "env": {
        "VAPI_TOKEN": "<token>",
        "OPENAI_API_KEY": "<key>"
      }
    }
  }
}

Prompt snapshots (prompt_diff) are stored in ./.standard-vocal/ of the directory you launch from β€” override with STANDARD_VOCAL_STATE_DIR.

Example: one call = one working agent

Code
deploy_agent({ vertical: "elagueur", company: "L'Arbre en Nord", extraKeywords: ["Coutiches", "Ramoniers"] })

β†’ creates the assistant with:

  • A receptionist prompt tuned for tree service (close after 3 questions, spelled-digit confirmation, urgency priority, never quote a price)
  • The right French voice (ElevenLabs)
  • Deepgram nova-3 with numerals: true, confidenceThreshold: 0.5, and keywords boosted for the business's real streets/towns

Then:

Code
run_eval({ assistantId: "...", vertical: "elagueur" })

β†’ runs 4 scripted scenarios (standard quote, address correction, storm emergency, solicitation) and returns a /100 grade with per-check pass/fail.

Eval report example

Code
GRADE: 100/100  β†’  PASS

[100/100] Devis classique (close rapide)
   βœ“ Demande le type de chantier
   βœ“ Demande l'adresse ou la ville
   βœ“ Confirme le numΓ©ro de rue en Γ©pelant les chiffres
   βœ“ Ne donne jamais de prix
   βœ“ ClΓ΄ture avec promesse de rappel

Architecture

Code
Claude / any MCP client
        β”‚  (stdio)
        β–Ό
standard-vocal-mcp  ──┬──► Vapi API (assistants, calls, recordings)
  (5 factory tools)   β”œβ”€β”€β–Ί OpenAI-compatible LLM (eval simulation)
                      └──► Vapi artifacts API (mono/customer/assistant WAV)
  • src/deploy.ts β€” vertical deployment
  • src/eval.ts β€” self-testing engine
  • src/forensics.ts β€” audio analysis
  • src/versioning.ts β€” prompt history / diff / rollback
  • verticals/templates.json β€” vertical templates (prompt, voice, transcriber, keywords)
  • evals/scenarios.json β€” scripted eval scenarios with weighted checks

Roadmap

  • More verticals (dentiste, kinΓ©, restaurant, garage)
  • regression_gate integration with CI (GitHub Action)
  • Call-level latency breakdown in audio_forensics
  • Hosted mcp-remote variant (like Vapi's mcp.vapi.ai)
  • Scenario auto-generation from real call transcripts

Built by Guillaume Jean Hussong Β· Standard Vocal β€” the phone agent factory.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

Last commit
29d ago
Most recent push to the default branch.
Tools exposed
6
Callable tools this server registers over MCP.

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Frequently Asked Questions about Standard Vocal MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "standard-vocal-mcp": { "command": "npx", "args": ["-y","standard-vocal-mcp"] } }

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Technical Specs & Signals

CategoryπŸ—£οΈConversational AI
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedAug 12, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit29d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 12, 2026
53Quality signal: Good Β· 53/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools26/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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