# Competitive Intelligence MCP Server (`martc03/competitive-intel-mcp`) Actor MCP server for competitive analysis. Gives AI assistants a unified view of any company by combining SEC filings, news articles, and federal contract awards. - **URL**: https://apify.com/martc03/competitive-intel-mcp.md - **Developed by:** [CoDee](https://apify.com/martc03) (community) - **Categories:** AI, Integrations - **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 0 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 ## Competitive Intelligence MCP Server Get SEC filings, news, and federal contract data for any company directly from your AI assistant. ### Overview This MCP (Model Context Protocol) server equips AI assistants with a multi-source competitive intelligence capability. Investigate any company by pulling together SEC EDGAR filings, recent news coverage, and federal contract awards into a unified view. Run individual queries against each data source or use the combined profile tool to get a full competitive snapshot in a single call. This server is designed for business analysts, investors, sales teams, procurement professionals, and anyone conducting market research or competitive analysis who needs fast, structured access to public company intelligence. ### Available Tools #### `intel_company_filings` Search SEC EDGAR for a company's regulatory filings. | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `companyName` | string | Yes | Company name or ticker symbol | | `formType` | string | No | SEC form type (e.g., `10-K`, `10-Q`, `8-K`, `DEF 14A`) | | `dateFrom` | string | No | Filing start date (YYYY-MM-DD) | | `dateTo` | string | No | Filing end date (YYYY-MM-DD) | | `limit` | number | No | Maximum results to return (default: 20) | **Returns**: entity name, form type, filing date, description, reporting period, file number, direct EDGAR URL. #### `intel_company_news` Search recent news articles mentioning a company. | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `query` | string | Yes | Company name or topic to search for | | `limit` | number | No | Maximum articles to return (default: 10) | **Returns**: article title, link, published date, source outlet name. #### `intel_company_contracts` Search federal contracts, grants, and loans awarded to a company via USASpending.gov. | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `companyName` | string | Yes | Company or recipient name | | `awardType` | string | No | One of: `contracts`, `grants`, `loans`, `all` (default: `all`) | | `dateFrom` | string | No | Award start date (YYYY-MM-DD) | | `dateTo` | string | No | Award end date (YYYY-MM-DD) | | `limit` | number | No | Maximum results to return (default: 20) | | `page` | number | No | Page number for pagination | **Returns**: award ID, recipient name, award amount, awarding agency, award type, start and end dates, description of work. #### `intel_company_profile` Generate a full competitive intelligence snapshot combining all data sources in one call. | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `companyName` | string | Yes | Company name to investigate | **Returns**: a combined object containing the 5 most recent SEC filings, 5 latest news articles, 5 recent federal contract awards, and summary statistics including total filing count, total contract count, and total contract dollar value. ### How to Connect Add this MCP server to your AI client configuration: ```json { "mcpServers": { "competitive-intel": { "type": "url", "url": "https://competitive-intel-mcp.apify.actor/mcp" } } } ```` ### Example Prompts - "Give me a full competitive intelligence profile on Palantir Technologies." - "What 10-K and 10-Q filings has Lockheed Martin submitted in the past year?" - "Find recent news articles about Rivian Automotive." - "How much has the Department of Defense awarded to Raytheon in federal contracts since 2023?" - "Search for 8-K filings from CrowdStrike to find any material event disclosures." - "Compare federal contract activity between Boeing and Northrop Grumman over the last two years." ### Pricing Each tool call costs **$0.01**. No subscriptions, no monthly fees. You only pay for the queries you run. ### Data Sources - **SEC EDGAR** ([efts.sec.gov](https://efts.sec.gov/)): The US Securities and Exchange Commission's full-text search system for public company filings. Covers all form types including annual reports, quarterly reports, proxy statements, and material event disclosures. - **Google News RSS**: Aggregated recent news coverage from thousands of media outlets worldwide, providing up-to-date press coverage and media mentions. - **USASpending.gov** ([api.usaspending.gov](https://api.usaspending.gov/)): The official source for federal spending data. Tracks all federal contracts, grants, loans, and other financial assistance awarded by US government agencies, with detailed recipient and transaction information. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("martc03/competitive-intel-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("martc03/competitive-intel-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 martc03/competitive-intel-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=martc03/competitive-intel-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Competitive Intelligence MCP Server", "description": "MCP server for competitive analysis. Gives AI assistants a unified view of any company by combining SEC filings, news articles, and federal contract awards.", "version": "0.1", "x-build-id": "PVovNOJfhRyGZyW5a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/martc03~competitive-intel-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-martc03-competitive-intel-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/martc03~competitive-intel-mcp/runs": { "post": { "operationId": "runs-sync-martc03-competitive-intel-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/martc03~competitive-intel-mcp/run-sync": { "post": { "operationId": "run-sync-martc03-competitive-intel-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": {} }, "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 } } } } } } } } } } ```