# MEDDIC Coach MCP — for AI Sales Agents + Human AEs (`elisabethhitz/meddic-coach-mcp`) Actor MEDDIC Coach MCP for AI sales agents and human AEs. Real-time MEDDIC scoring, next-best-question, deal probability, close-play recommendations, mutual action plan generation. The SaaS-sales gold-standard methodology. - **URL**: https://apify.com/elisabethhitz/meddic-coach-mcp.md - **Developed by:** [Elisabeth](https://apify.com/elisabethhitz) (community) - **Categories:** AI, Agents, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 meddic tool calls This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## MEDDIC Coach MCP MCP server for AI sales agents and human account executives. Six tools built on the MEDDIC / MEDDPICC qualification methodology, the B2B SaaS gold standard. ### Tools | Tool | What it does | |---|---| | `score_meddic_completion` | Heuristic 0-100 score per MEDDIC pillar from a free-text deal summary, plus the weakest pillar to work next | | `suggest_next_question` | Next-best discovery questions for any pillar (M, E, D1, D2, I, C, P, CC) | | `identify_economic_buyer` | EB signals, discovery questions, and how to distinguish the EB from procurement or a champion | | `score_deal_probability` | Probability banding from pillar scores, for pipeline prioritization | | `recommend_close_play` | A specific close play for 5 common deal states, with the risk to watch | | `generate_mutual_action_plan` | A 6-section Mutual Action Plan template plus a send-ready message to your champion | ### Use as MCP server (Standby mode) Endpoint: `https://elisabethhitz--meddic-coach-mcp.apify.actor/mcp` Authenticate with your Apify token: `Authorization: Bearer `. Works with any MCP client that supports Streamable HTTP (Claude, custom agents, etc.). ### Use as a one-shot actor Set `tool` in the input plus that tool's parameters, run the actor, and read the result from the default dataset. ### Who built this Built by [Elisabeth Hitz](https://closermethod.com): 6 years carrying B2B SaaS quota (268% at Deel, 167% at Criteo), now building AI systems for sales teams. Structured frameworks based on publicly documented MEDDIC methodology. Not a substitute for human qualification. Verify deal-critical data with your prospect directly. # Actor input Schema ## `tool` (type: `string`): Which MEDDIC Coach tool to call ## `weakest_pillar` (type: `string`): MEDDIC pillar to strengthen (used by suggest_next_question) ## `deal_summary` (type: `string`): Free-text summary of where the deal stands (used by score_meddic_completion) ## `deal_state` (type: `string`): Current deal state (used by recommend_close_play) ## `deal_context` (type: `string`): Brief deal context (used by generate_mutual_action_plan) ## `account_context` (type: `string`): Account context (used by identify_economic_buyer) ## Actor input object example ```json { "tool": "suggest_next_question", "weakest_pillar": "M", "deal_summary": "Champion confirmed, economic buyer identified but not engaged, pain quantified at 40 hours/week, timeline Q4.", "deal_state": "champion_strong_eb_weak", "deal_context": "Mid-market SaaS deal, security review required, target signature this quarter", "account_context": "200-person fintech, talking to RevOps lead" } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tool": "suggest_next_question" }; // Run the Actor and wait for it to finish const run = await client.actor("elisabethhitz/meddic-coach-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "tool": "suggest_next_question" } # Run the Actor and wait for it to finish run = client.actor("elisabethhitz/meddic-coach-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "tool": "suggest_next_question" }' | apify call elisabethhitz/meddic-coach-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=elisabethhitz/meddic-coach-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MEDDIC Coach MCP — for AI Sales Agents + Human AEs", "description": "MEDDIC Coach MCP for AI sales agents and human AEs. Real-time MEDDIC scoring, next-best-question, deal probability, close-play recommendations, mutual action plan generation. The SaaS-sales gold-standard methodology.", "version": "1.0", "x-build-id": "dYLKQH8XFIXdxyziM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/elisabethhitz~meddic-coach-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-elisabethhitz-meddic-coach-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/elisabethhitz~meddic-coach-mcp/runs": { "post": { "operationId": "runs-sync-elisabethhitz-meddic-coach-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/elisabethhitz~meddic-coach-mcp/run-sync": { "post": { "operationId": "run-sync-elisabethhitz-meddic-coach-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "tool" ], "properties": { "tool": { "title": "Tool", "enum": [ "score_meddic_completion", "suggest_next_question", "identify_economic_buyer", "score_deal_probability", "recommend_close_play", "generate_mutual_action_plan" ], "type": "string", "description": "Which MEDDIC Coach tool to call", "default": "suggest_next_question" }, "weakest_pillar": { "title": "Weakest pillar", "enum": [ "M", "E", "D1", "D2", "I", "C", "P", "CC" ], "type": "string", "description": "MEDDIC pillar to strengthen (used by suggest_next_question)", "default": "M" }, "deal_summary": { "title": "Deal summary", "type": "string", "description": "Free-text summary of where the deal stands (used by score_meddic_completion)", "default": "Champion confirmed, economic buyer identified but not engaged, pain quantified at 40 hours/week, timeline Q4." }, "deal_state": { "title": "Deal state", "enum": [ "late_stage_high_meddic", "champion_strong_eb_weak", "pain_unclear", "no_decision_process", "competitor_winning" ], "type": "string", "description": "Current deal state (used by recommend_close_play)", "default": "champion_strong_eb_weak" }, "deal_context": { "title": "Deal context", "type": "string", "description": "Brief deal context (used by generate_mutual_action_plan)", "default": "Mid-market SaaS deal, security review required, target signature this quarter" }, "account_context": { "title": "Account context", "type": "string", "description": "Account context (used by identify_economic_buyer)", "default": "200-person fintech, talking to RevOps lead" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```