# Sales-Engineering / RFP MCP for AI Agents (`elisabethhitz/sales-engineering-mcp`) Actor MCP for AI sales agents handling RFPs + security questionnaires. Templates for SOC2, ISO27001, HIPAA, GDPR, NIST CSF, PCI-DSS attestations + 10 questionnaire topics + 7 RFP sections + 3 battlecards. Disclaimer: starting templates, not legal advice. - **URL**: https://apify.com/elisabethhitz/sales-engineering-mcp.md - **Developed by:** [Elisabeth](https://apify.com/elisabethhitz) (community) - **Categories:** AI, Automation, Jobs - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Sales-Engineering / RFP-Response MCP **Drop-in templates for security questionnaires (SOC2, ISO27001, HIPAA, GDPR, NIST CSF, PCI-DSS), RFP responses, and competitive battlecards. For AI sales agents handling sales-engineering workload.** By [Elisabeth Hitz](https://www.elisabethhitz.com) — 10+ years B2B enterprise sales experience. > **Disclaimer.** Templates and reference frameworks. Not legal, security, or compliance certification. Always validate responses against your actual security posture. --- ### What This Solves AI sales agents can write emails. Sales engineering work — security questionnaires, RFP responses, competitive battlecards, technical objections — requires structured templates and reasoning that LLMs alone hallucinate. This MCP gives AI agents callable sales-engineering primitives. ### Tools (6) 1. `get_security_questionnaire_template` — SOC2, ISO27001, HIPAA, GDPR, NIST CSF, PCI-DSS 2. `get_rfp_response_section` — pre-templated answers for 30+ common RFP questions 3. `get_competitive_battlecard` — top 5 competitors per category with positioning frame 4. `handle_technical_objection` — security, data residency, integration, scalability, latency 5. `generate_pricing_justification` — TCO, ROI, payback frames 6. `recommend_proof_artifact` — SOC2 report, pen-test, customer reference, architecture diagram ### Coverage - 6 major compliance frameworks - 30+ RFP question types - 5 competitive categories with battlecards - 25+ technical objection types ### Use Cases - AI sales engineering agents reducing manual SE workload - AI customer-success agents handling renewals + objections - B2B SaaS sales agents responding to RFPs faster - Enterprise SaaS founders augmenting their SE team with AI ### Pricing Pay-per-event. Compute units billed per Apify standard. ### Integration MCP-compatible: Claude Desktop, Cursor, Cline, Continue, VS Code Copilot Agent, custom MCP clients. ### License MIT. ### Author Elisabeth Hitz — Swiss-American B2B sales executive based in Barcelona. 10+ years closing enterprise deals across ad-tech, SaaS, media, finance, and global hiring. 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`): Which EOR Compliance tool to call ## `country` (type: `string`): Country (required for most tools) ## `headcount` (type: `integer`): Number of employees you plan to have in this country ## `annual_salary_usd` (type: `integer`): Average gross annual salary per employee in USD ## `objection_type` (type: `string`): Type of EOR sales objection ## 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/sales-engineering-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/sales-engineering-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/sales-engineering-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=elisabethhitz/sales-engineering-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sales-Engineering / RFP MCP for AI Agents", "description": "MCP for AI sales agents handling RFPs + security questionnaires. Templates for SOC2, ISO27001, HIPAA, GDPR, NIST CSF, PCI-DSS attestations + 10 questionnaire topics + 7 RFP sections + 3 battlecards. Disclaimer: starting templates, not legal advice.", "version": "0.0", "x-build-id": "cnHo3SrZa7NQxt8nl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/elisabethhitz~sales-engineering-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-elisabethhitz-sales-engineering-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~sales-engineering-mcp/runs": { "post": { "operationId": "runs-sync-elisabethhitz-sales-engineering-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~sales-engineering-mcp/run-sync": { "post": { "operationId": "run-sync-elisabethhitz-sales-engineering-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_country_eor_brief", "get_misclassification_risk", "get_eor_vs_entity_calc", "get_termination_rules", "get_statutory_benefits", "get_visa_paths", "get_eor_objection_handler", "get_full_eor_pack" ], "type": "string", "description": "Which EOR Compliance tool to call" }, "country": { "title": "Country", "enum": [ "uk", "ireland", "spain", "germany", "france", "netherlands", "nordics", "mexico", "brazil", "india", "japan", "singapore" ], "type": "string", "description": "Country (required for most tools)" }, "headcount": { "title": "Planned headcount in country (12 months)", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Number of employees you plan to have in this country" }, "annual_salary_usd": { "title": "Average annual salary (USD)", "minimum": 10000, "maximum": 1000000, "type": "integer", "description": "Average gross annual salary per employee in USD" }, "objection_type": { "title": "EOR Objection", "enum": [ "use_contractors_instead", "not_ready_to_commit", "too_expensive", "we_have_entity", "compliance_unclear", "termination_concerns", "data_privacy_worry", "competitor_offer" ], "type": "string", "description": "Type of EOR sales objection" } } }, "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 } } } } } } } } } } ```