# MCP: SEO Keyword & Search-Demand Intel (`seibs.co/mcp-keyword-demand-intel`) Actor MCP server for keyword-demand-intel. AI-agent tools: expand\_keywords, keyword\_metrics, analyze\_serp, keyword\_difficulty, trend\_signal across Google autosuggest, logged-out SERP, and Trends. x402 (USDC on Base) and Skyfire ready. For SEO, content, and agentic research. - **URL**: https://apify.com/seibs.co/mcp-keyword-demand-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** MCP servers, SEO tools, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 mcp tool calls 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 ## MCP: SEO Keyword & Search-Demand Intel An [MCP](https://modelcontextprotocol.io) server wrapping **[keyword-demand-intel](https://apify.com/seibs.co/keyword-demand-intel)** so AI agents can pull keyword and search-demand intelligence as native tool calls. x402 (USDC on Base) and Skyfire agentic-payment ready. ### Tools | Tool | What it does | |---|---| | `expand_keywords` | Expand a seed into its long-tail universe via Google autosuggest, with intent + estimated volume + proxy difficulty per keyword. | | `keyword_metrics` | The full bundle for a seed: expansion + volume + difficulty + live SERP features + topic clusters (and optional Trends seasonality). | | `analyze_serp` | Fetch the logged-out Google SERP for one query: top results, SERP features, People-Also-Ask, related searches, and difficulty. | | `keyword_difficulty` | Score ranking difficulty (0-100) for a keyword from its live SERP - authority domains, ads, and featured-snippet pressure. | | `trend_signal` | A Google Trends seasonality signal for a keyword (rising / falling / flat / seasonal, peak month, spikiness). Best-effort. | ### Modes - `list_tools` - emit the tool catalog (name, description, JSON input schema) plus the agentic-payment descriptor. Free. - `call_tool` - invoke one tool. Requires `tool` + `args`. - `batch` - invoke up to 10 `{tool, args}` calls in one run. ```json { "mode": "call_tool", "tool": "expand_keywords", "args": { "seed": "crm software", "country": "US", "limit": 80 } } ```` ### How it works Each tool calls the upstream `seibs.co/keyword-demand-intel` actor via `Actor.call()`, reads its dataset, and reshapes the records into compact, deterministic JSON (`{ tool, args, ok, count, items, summary, error }`). Tools never throw - upstream failure or a blocked SERP becomes `ok: false` with a documented `error`. ### Pricing Flat **$0.005 per MCP tool call** (`mcp_tool_call`). The upstream actor charges its own pay-per-event events (`keyword_record` $0.003, `volume_estimate` $0.006, `serp_snapshot` $0.008) directly to the same run as pass-through, so total cost = $0.005 per call + upstream usage. x402 / Skyfire enable token-less per-call payment for direct AI agents when MCP monetization is enabled. ### Posture Logged-out public Google surfaces only (autosuggest, the public SERP, public Trends), rate-limited, no PII. Volume and difficulty are clearly-labeled estimates from a published model, not Google's gated Keyword Planner figures. See the [main actor](https://apify.com/seibs.co/keyword-demand-intel) for the full source and legal-safety notes. # Actor input Schema ## `mode` (type: `string`): list\_tools = emit the MCP tool catalog (free). call\_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls. ## `tool` (type: `string`): Required when mode=call\_tool. One of: expand\_keywords, keyword\_metrics, analyze\_serp, keyword\_difficulty, trend\_signal. ## `args` (type: `object`): Arguments for the selected tool. Example for expand\_keywords: {"seed": "crm software", "country": "US", "limit": 80}. ## `calls` (type: `array`): Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: \[{"tool": "expand\_keywords", "args": {"seed": "crm software"}}, {"tool": "analyze\_serp", "args": {"term": "best crm software"}}]. Max 10 calls per run. ## Actor input object example ```json { "mode": "list_tools", "tool": "expand_keywords", "args": { "seed": "crm software", "country": "US", "limit": 80 }, "calls": [] } ``` # 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 = { "mode": "list_tools", "args": { "seed": "crm software", "country": "US", "limit": 80 } }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/mcp-keyword-demand-intel").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 = { "mode": "list_tools", "args": { "seed": "crm software", "country": "US", "limit": 80, }, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/mcp-keyword-demand-intel").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 '{ "mode": "list_tools", "args": { "seed": "crm software", "country": "US", "limit": 80 } }' | apify call seibs.co/mcp-keyword-demand-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/mcp-keyword-demand-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP: SEO Keyword & Search-Demand Intel", "description": "MCP server for keyword-demand-intel. AI-agent tools: expand_keywords, keyword_metrics, analyze_serp, keyword_difficulty, trend_signal across Google autosuggest, logged-out SERP, and Trends. x402 (USDC on Base) and Skyfire ready. For SEO, content, and agentic research.", "version": "0.1", "x-build-id": "ZkxOLBDPlLGtN1UwA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~mcp-keyword-demand-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-mcp-keyword-demand-intel", "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/seibs.co~mcp-keyword-demand-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-mcp-keyword-demand-intel", "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/seibs.co~mcp-keyword-demand-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-mcp-keyword-demand-intel", "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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "list_tools", "call_tool", "batch" ], "type": "string", "description": "list_tools = emit the MCP tool catalog (free). call_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls.", "default": "list_tools" }, "tool": { "title": "Tool name", "enum": [ "expand_keywords", "keyword_metrics", "analyze_serp", "keyword_difficulty", "trend_signal" ], "type": "string", "description": "Required when mode=call_tool. One of: expand_keywords, keyword_metrics, analyze_serp, keyword_difficulty, trend_signal.", "default": "expand_keywords" }, "args": { "title": "Tool arguments (JSON object)", "type": "object", "description": "Arguments for the selected tool. Example for expand_keywords: {\"seed\": \"crm software\", \"country\": \"US\", \"limit\": 80}." }, "calls": { "title": "Batch calls", "maxItems": 10, "type": "array", "description": "Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: [{\"tool\": \"expand_keywords\", \"args\": {\"seed\": \"crm software\"}}, {\"tool\": \"analyze_serp\", \"args\": {\"term\": \"best crm software\"}}]. Max 10 calls per run.", "default": [] } } }, "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 } } } } } } } } } } ```