# SMB Sales Intelligence MCP for AI SDRs (`elisabethhitz/smb-sales-intelligence-mcp`) Actor B2B sales playbook frameworks for AI agents. 10 callable tools: discovery scripts, objection handlers, follow-up sequences, 7 closing styles, 3-option pricing, EMEA intelligence (UK/IE/ES/DE/FR/NL/Nordics), cold email + call scripts. Built from 10+ yrs B2B enterprise sales. Not legal advice. - **URL**: https://apify.com/elisabethhitz/smb-sales-intelligence-mcp.md - **Developed by:** [Elisabeth](https://apify.com/elisabethhitz) (community) - **Categories:** AI, Automation, Lead generation - **Stats:** 0 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## SMB Sales Intelligence MCP for AI SDRs **B2B sales playbook frameworks for AI agents.** 10 callable tools: discovery scripts, objection handlers, follow-up sequences, 7 closing styles, 3-option pricing, EMEA intelligence, qualification scoring, deal-stage progression. By [Elisabeth Hitz](https://www.elisabethhitz.com) — 10+ years B2B enterprise sales experience across ad-tech, media, finance, advertising, and SaaS. Five consecutive years overshooting quota. > **Disclaimer.** Frameworks for educational and operational guidance. Not legal or compliance advice. Always validate with internal stakeholders before customer deployment. --- ### What This Solves Your AI SDR is fluent at language. The bottleneck is structured reasoning over B2B context: which objection to handle, when to expand, how to qualify, when to walk away. This MCP gives the AI agent a callable layer of operator-grade sales playbooks, normalized into structured output every time. ### Tools (10) 1. `get_discovery_script` — 7-question framework for any vertical 2. `handle_objection` — 25+ objection types with country-specific variants 3. `score_qualification` — BANT, MEDDIC, GPCT scoring 4. `recommend_follow_up_sequence` — 3-touch / 7-touch / 14-touch cadences 5. `get_closing_style` — Assumptive, Alternative, Urgency, Summary, Question, Sharp-Angle, Takeaway 6. `generate_three_option_pricing` — Anchor + plus + premium frame 7. `progress_deal_stage` — From discovery → close, with exit criteria 8. `get_emea_country_brief` — UK/IE/ES/DE/FR/NL/Norway sales nuances 9. `analyze_competitor_objection` — Common rival positioning frames 10. `recommend_close_play` — Deal-state-aware recommendation ### Use Cases - **AI SDR platforms** (11x, Regie, Ava, Persana, Bardeen, Alice) — give your agents structured B2B intuition - **CRM-embedded AI** — call from Salesforce / HubSpot AI agents - **In-house AI ops teams** building proprietary sales agents - **B2B SaaS founders** training their own GTM agents ### Pricing Pay-per-event. Charged when the actor starts running. Compute units billed per Apify standard. ### Integration MCP-compatible: Claude Desktop, Cursor, Cline, Continue, VS Code Copilot Agent, custom MCP clients. ```` import { CallableSDR } from '@elisabethhitz/mcp-clients'; const sdr = new CallableSDR({ apifyToken: process.env.APIFY\_TOKEN }); const objection = await sdr.handle\_objection({ vertical: 'b2b-saas', stage: 'discovery', message: '...' }); ```` ### License MIT. ### Author Elisabeth Hitz — Swiss-American B2B sales executive based in Barcelona. 10+ years closing enterprise deals across ad-tech, media, finance, advertising, and SaaS. Five consecutive years overshooting quota. NYU graduate. Building MCP infrastructure for AI sales agents. - [elisabethhitz.com](https://www.elisabethhitz.com) - [LinkedIn](https://www.linkedin.com/in/elisabethhitz) # Actor input Schema ## `tool` (type: `string`): The MCP tool to call ## `tone` (type: `string`): The tone of the discovery script ## `objection_type` (type: `string`): The type of objection to handle ## `sequence_type` (type: `string`): The type of follow-up sequence ## `style` (type: `string`): The closing style to use ## `country` (type: `string`): The European market to get intelligence for ## `template_type` (type: `string`): The type of cold email template ## `call_type` (type: `string`): The type of call script ## Actor input object example ```json {} ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("elisabethhitz/smb-sales-intelligence-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 = {} # Run the Actor and wait for it to finish run = client.actor("elisabethhitz/smb-sales-intelligence-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 '{}' | apify call elisabethhitz/smb-sales-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=elisabethhitz/smb-sales-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SMB Sales Intelligence MCP for AI SDRs", "description": "B2B sales playbook frameworks for AI agents. 10 callable tools: discovery scripts, objection handlers, follow-up sequences, 7 closing styles, 3-option pricing, EMEA intelligence (UK/IE/ES/DE/FR/NL/Nordics), cold email + call scripts. Built from 10+ yrs B2B enterprise sales. Not legal advice.", "version": "0.0", "x-build-id": "toIoa3jj47bUcavVU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/elisabethhitz~smb-sales-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-elisabethhitz-smb-sales-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/elisabethhitz~smb-sales-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-elisabethhitz-smb-sales-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/elisabethhitz~smb-sales-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-elisabethhitz-smb-sales-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "get_discovery_script", "get_objection_response", "get_followup_sequence", "get_closing_script", "get_pricing_framework", "get_emea_intelligence", "get_cold_email_template", "get_call_script", "get_buying_signals", "get_full_playbook" ], "type": "string", "description": "The MCP tool to call" }, "tone": { "title": "Tone (for discovery scripts)", "enum": [ "professional", "warm", "ultra_short", "cold_outbound", "inbound_lead" ], "type": "string", "description": "The tone of the discovery script" }, "objection_type": { "title": "Objection Type", "enum": [ "too_expensive", "no_budget", "need_approval", "comparing_options", "bad_timing", "already_have_someone", "send_info", "need_to_think", "too_soon", "ghosting" ], "type": "string", "description": "The type of objection to handle" }, "sequence_type": { "title": "Sequence Type", "enum": [ "post_proposal", "post_call", "cold_outbound", "revival" ], "type": "string", "description": "The type of follow-up sequence" }, "style": { "title": "Closing Style", "enum": [ "standard", "assumptive", "timeline", "scarcity", "retainer", "choice", "next_step" ], "type": "string", "description": "The closing style to use" }, "country": { "title": "EMEA Country", "enum": [ "uk", "ireland", "spain", "germany", "france", "netherlands", "nordics" ], "type": "string", "description": "The European market to get intelligence for" }, "template_type": { "title": "Cold Email Template Type", "enum": [ "pattern_interrupt", "observation", "mutual_connection", "case_study", "breakup" ], "type": "string", "description": "The type of cold email template" }, "call_type": { "title": "Call Type", "enum": [ "discovery_call", "cold_call" ], "type": "string", "description": "The type of call script" } } }, "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 } } } } } } } } } } ```