# Competitive Intelligence MCP Server (`pxlbyt-co/competitive-intel-mcp`) Actor Analyze markets with live competitive intelligence, pricing scans, complaint mining, and gap detection. Includes `analyze-market` ($0.50) and `quick-scan` ($0.10) for founders, strategists, product teams, and agent workflows. - **URL**: https://apify.com/pxlbyt-co/competitive-intel-mcp.md - **Developed by:** [PixelByte Labs](https://apify.com/pxlbyt-co) (community) - **Categories:** MCP servers, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $500.00 / 1,000 full market analyses 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 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 — AI Market Analysis MCP Server ### What is the Competitive Intelligence MCP Server? The **Competitive Intelligence MCP Server** analyzes any market or niche using live web research. It discovers competitors, maps pricing landscapes, analyzes sentiment, identifies market gaps, and returns a **scored opportunity assessment** from a single focused MCP tool call. It is built for **Claude Code, Claude Desktop, OpenClaw, Cursor, and other MCP workflows** where you want one clean market-analysis step instead of a long wandering research chat. It also fits automation-adjacent workflows well: run one structured market scan, then let the rest of your agent stack use the results for positioning, pricing, or GTM work. Stop guessing whether a market is too crowded. Get structured, evidence-based competitive analysis with **real data from live web searches**, not hallucinated competitor lists or a long manual loop. 🔍 **Live competitor discovery** — finds 5-12 real competitors via multi-stage web research. 💰 **Pricing landscape** — maps what competitors charge and identifies pricing gaps. 😤 **Sentiment analysis** — surfaces real customer complaints and unmet needs. 📊 **Opportunity scoring** — scores market attractiveness across 4 dimensions. 🎯 **Gap analysis** — identifies underserved segments and positioning opportunities. ### What can this Competitive Intelligence Actor do? - ✅ **Full market analysis** — multi-stage research pipeline: discovery → sentiment → gap analysis → opportunity scoring - ✅ **Quick competitor scan** — fast check of competition level (open-field / light / moderate / crowded / red-ocean) - ✅ **Competitor profiling** — name, URL, pricing, positioning, strengths, weaknesses for each competitor found - ✅ **Pricing comparison** — structured pricing landscape showing what the market charges - ✅ **Review mining** — finds real customer complaints and feature gaps from review sites and communities - ✅ **Actionable recommendations** — not just data, but specific positioning and differentiation advice - ✅ **Depth control** — choose quick, standard, or deep analysis depending on your needs - ✅ **Works via MCP** — connect from Claude Code, Claude Desktop, OpenClaw, Cursor, Windsurf, or any MCP client - ✅ **Fits multi-agent workflows** — let one agent gather the market structure, then let another turn it into positioning, pricing, or GTM output Runs on the **Apify platform**. Schedule recurring market scans, trigger analyses via API, integrate results into your product workflow, or monitor markets automatically with webhooks. ### What data does the Competitive Intelligence tool extract? | Data Point | Description | |-----------|-------------| | Competitor Profiles | Name, URL, pricing, positioning, strengths, weaknesses | | Competition Level | open-field / light / moderate / crowded / red-ocean | | Pricing Landscape | Price ranges, models (subscription/usage/one-time), free tier availability | | Customer Complaints | Real negative reviews, unmet needs, frustration points | | Market Gaps | Underserved segments, feature gaps, pricing gaps | | Opportunity Score | Market attractiveness (1-10) across 4 dimensions | | Market Maturity | Early / growing / mature / declining | | Recommendations | Specific positioning, differentiation, and entry strategies | ### How to analyze a market with MCP 1. **Connect this Actor** to your MCP client (Claude Code, Claude Desktop, OpenClaw, Cursor, Windsurf, or any MCP-compatible tool) 2. **Ask your AI assistant** to analyze a market — e.g. "Analyze the competitive landscape for AI code review tools" 3. **Optionally add context** — competitors you already know, your purpose (entering the market? monitoring it?), desired depth 4. **Review the report** — competitor profiles, pricing landscape, gaps, opportunity scores, and recommendations 5. **Take action** — use the gaps and positioning advice to differentiate your product #### Example workflow: one market scan, then downstream execution One practical Claude-style workflow: 1. Your assistant calls `analyze-market` on a niche you are considering 2. It gets back competitors, pricing, complaints, and market gaps in one structured payload 3. In the same conversation, a second step turns that output into positioning, pricing, or GTM drafts 4. On a schedule, you can rerun the scan and compare deltas instead of repeating open-ended market research That replaces a longer agent loop of manually discovering competitors, checking pricing pages, hunting complaints, and then summarizing the market from scratch each time. You can also call the API directly: ```` POST https://YOUR\_USERNAME--competitive-intel-mcp.apify.actor/mcp ``` ### MCP Tools #### `analyze-market` — Full Competitive Analysis ($0.50) Multi-stage research pipeline producing a comprehensive competitive report. **Research stages:** 1. **Discovery** — 3-4 searches to find direct competitors, comparison sites, community recommendations 2. **Sentiment** — Review and complaint analysis for top competitors 3. **Gap analysis** — Searches for unmet needs, feature gaps, pricing gaps 4. **Scoring** — Algorithmic market opportunity scoring **Input:** | Parameter | Required | Description | |-----------|----------|-------------| | `market` | ✅ | Market or niche to analyze (e.g. "AI code review tools") | | `purpose` | Optional | Why you need this report (entering market, monitoring, etc.) | | `knownCompetitors` | Optional | Competitors you already know about | | `depth` | Optional | quick / standard / deep (default: standard) | **Output:** Full markdown report with competitor profiles, pricing landscape, sentiment findings, gaps, opportunity scores, and positioning recommendations. #### `quick-scan` — Fast Competition Check ($0.10) Lightweight scan to quickly assess competition level. Use this to screen markets before investing in a full analysis. **Input:** | Parameter | Required | Description | |-----------|----------|-------------| | `market` | ✅ | Market or niche to scan | **Output:** Competition level assessment + competitor list. **Competition levels:** | Level | Meaning | |-------|---------| | 🟢 Open field | Few or no competitors — first-mover opportunity | | 🟡 Light | Some players but room for differentiation | | 🟠 Moderate | Established players, need clear differentiation | | 🔴 Crowded | Many competitors, hard to stand out | | ⛔ Red ocean | Saturated, commodity pricing, avoid unless you have a unique angle | ### How much does competitive intelligence cost? | Tool | Price | Brave Search Cost | Total | |------|-------|-------------------|-------| | `analyze-market` | $0.50 | ~$0.03 (5-6 searches) | ~$0.53 | | `quick-scan` | $0.10 | ~$0.005 (1 search) | ~$0.105 | **Compare this to alternatives:** - Professional competitive analysis: $2,000-25,000 - Competitive intelligence SaaS (Crayon, Klue): $500-2,000/month - Fiverr competitive analysis: $50-200 - Doing it yourself manually: 4-8 hours of research For $0.50, you get a structured report with real competitor data. In practice, that is often cheaper than letting Claude Code or another agent spend a long session discovering competitors, pricing, complaints, and gaps from scratch. Scan 10 markets for $5. Apify's **free tier includes $5/month** in platform credits — enough for ~9 full analyses or 45 quick scans per month at no cost. ### Trust and transparency This tool is deliberately explicit about what it knows: - **Live search is visible, not implied.** With `BRAVE_API_KEY`, it uses live research to find competitors, pricing, complaints, and gaps. - **Degraded mode is honest.** If live search is unavailable, the tool says that directly instead of implying there are no competitors or no market signal. - **Structured output is inspectable.** You get named competitors, pricing patterns, complaints, and gaps you can review, not just a confident paragraph. That transparency matters for developer buyers who are tired of black-box market analysis. ### Example output **Input:** > Market: AI-powered code review tools > Known competitors: CodeRabbit, Sourcery **Output (abbreviated):** ``` ### Competitive Intelligence Report: AI Code Review Tools #### Competition Level: 🟠 MODERATE (6 competitors found) #### Competitor Profiles | Competitor | Pricing | Positioning | Key Strength | |-----------|---------|-------------|-------------| | CodeRabbit | $12-24/user/mo | AI PR reviewer | Deep GitHub integration | | Sourcery | Free-$30/user/mo | Code quality AI | Python-first, freemium | | Codacy | $15/user/mo | Automated reviews | Multi-language, enterprise | | DeepCode (Snyk) | Acquired | Security-focused | Vulnerability detection | | Qodo (CodiumAI) | Free-custom | AI test generation | Test-first approach | | Codeium | Free-$12/mo | AI completion + review | Generous free tier | #### Pricing Landscape - Range: $0 (freemium) to $30/user/month - Enterprise: Custom pricing, $50-100+/user - Most offer free tiers — table-stakes for adoption #### Top Customer Complaints (from reviews) - "Too many false positives" (CodeRabbit, Codacy) - "Doesn't understand our codebase context" (multiple) - "Enterprise pricing is opaque" (Codacy, Qodo) #### Market Gaps - 🎯 Framework-specific reviews (Rails, Django conventions) - 🎯 Architecture-level feedback (not just line-by-line) - 🎯 Transparent enterprise pricing #### Opportunity Score: 5.8/10 Moderate opportunity. Established players, but clear gaps in specialization and context-awareness. ```` ### Setup — Brave Search API key For live market research (strongly recommended), configure `BRAVE_API_KEY`: 1. Get a free API key at [brave.com/search/api](https://brave.com/search/api/) — includes $5/month credits (~1,000 searches) 2. In Apify Console → this Actor → Settings → Environment Variables → add `BRAVE_API_KEY` Without the key, tools explicitly say live search is unavailable instead of implying the market has no competitors. ### Optional setup — AI synthesis For provider-agnostic AI synthesis, configure either: #### Preferred generic config - `LLM_PROVIDER` = `openai` or `anthropic` - `LLM_API_KEY` - `LLM_MODEL` - `LLM_BASE_URL` (optional, for custom / compatible runtimes) #### Backward-compatible provider-specific config - `OPENAI_API_KEY`, `OPENAI_MODEL`, `OPENAI_BASE_URL` - or `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL` If no AI provider is configured, the server still works with heuristic scoring only. ### FAQ **How many competitors does it find?** Typically 5-12 for `analyze-market`, depending on the market. The tool searches multiple angles (direct competitors, comparison sites, community recommendations). **Can I monitor a market over time?** Yes! Use Apify's built-in scheduler to run `analyze-market` weekly or monthly. Compare reports to spot new entrants, pricing changes, and shifting gaps. **Does it work for non-tech markets?** Yes. It searches the web for competitors in any space — SaaS, physical products, services, local businesses, creative niches. **How does depth affect results?** - **quick** — fewer searches, faster, good for initial screening - **standard** — balanced coverage (default) - **deep** — more searches, more competitors, fuller sentiment analysis **Is my data stored?** No. Inputs and outputs are processed in your Apify run and not retained beyond your own run history. ### Related tools - [Business Idea Validator MCP Server](https://apify.com/YOUR_USERNAME/idea-validator-mcp) — Validate business ideas with an 8-dimension scoring framework before you start building # 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("pxlbyt-co/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("pxlbyt-co/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 pxlbyt-co/competitive-intel-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pxlbyt-co/competitive-intel-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Competitive Intelligence MCP Server", "description": "Analyze markets with live competitive intelligence, pricing scans, complaint mining, and gap detection. Includes `analyze-market` ($0.50) and `quick-scan` ($0.10) for founders, strategists, product teams, and agent workflows.", "version": "1.0", "x-build-id": "yaAYv5bGKYr4QMAp7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pxlbyt-co~competitive-intel-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pxlbyt-co-competitive-intel-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/pxlbyt-co~competitive-intel-mcp/runs": { "post": { "operationId": "runs-sync-pxlbyt-co-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/pxlbyt-co~competitive-intel-mcp/run-sync": { "post": { "operationId": "run-sync-pxlbyt-co-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 }, 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