# SERP Research Pack for AI Agents (`iammienta/agent-serp-research-pack`) Actor Turn a search query into an agent-ready SERP research brief. Returns source URLs, findings, extracted entities, risks, and suggested next searches for market research, SEO planning, content briefs, and tool discovery. - **URL**: https://apify.com/iammienta/agent-serp-research-pack.md - **Developed by:** [Pete Mientkiewicz](https://apify.com/iammienta) (community) - **Categories:** AI, Agents, SEO tools - **Stats:** 2 total users, 1 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 ## SERP Research Pack for AI Agents SERP Research Pack for AI Agents turns a natural-language search query into a structured research brief that another AI agent can consume directly. It returns deduped source records, snippets, inspected-page summaries, evidence sentences, relevance scores, entities, research risks, and suggested next searches. Use it as a bounded discovery step before an agent writes a market brief, selects tools, plans a scrape, prepares an SEO outline, researches competitors, or gathers cited source leads for a human analyst. ### Use This Actor When - You need a compact SERP research brief instead of raw browser output. - You want source URLs, snippets, evidence sentences, and relevance signals in one JSON record. - You are building an AI-agent workflow that needs search context before deeper crawling or summarisation. - You need quick market, product, competitor, topic, or tool-discovery research. - You want next-search suggestions when the first result set is weak or incomplete. ### Do Not Use This Actor When - You need guaranteed Google ranking data, rank tracking, or paid keyword volume. - You need exhaustive coverage across every search engine or country. - You need facts from paywalled, login-only, or heavily blocked pages. - You need legally or commercially verified facts without human review. ### Input ```json { "query": "AI agents Apify actors MCP", "market": "UK", "maxResults": 8, "maxPagesToInspect": 5 } ```` #### Input Fields - `query`: Required. Natural-language research query. - `market`: Optional. Market, country, language, or location context appended to the query. - `maxResults`: Optional. Number of search results to return, from 1 to 20. - `maxPagesToInspect`: Optional. Number of result pages to fetch for summaries and evidence, from 0 to 10. Use `0` for SERP-only output. ### Output The Actor writes one JSON research-pack record to the default dataset. Key fields: - `query`: The final query used. - `summary`: Counts, average relevance, source-type mix, and `dataQuality`. - `sourceRecords`: Inspected sources with `rank`, `title`, `url`, `domain`, `snippet`, `status`, `sourceType`, `pageSummary`, `evidence`, `relevanceScore`, and fetch status. - `uninspectedResults`: Extra deduped SERP results that were not fetched. - `topFindings`: Evidence-backed finding seeds with source URLs and confidence labels. - `entities`: Extracted terms useful for follow-up research. - `risks`: Data-quality and interpretation warnings. - `nextSearches`: Follow-up searches an agent can run. - `downstreamWorkflowHints`: Suggested next actions for agent pipelines. - `limitations`: Explicit boundaries of the result. Example shape: ```json { "query": "AI agents Apify actors MCP UK", "summary": { "inspectedSources": 5, "returnedResults": 8, "averageRelevance": 0.58, "dataQuality": "medium", "sourceTypeCounts": { "technical_reference": 2, "guide_or_article": 2, "general_source": 1 } }, "sourceRecords": [ { "rank": 1, "title": "Example source", "url": "https://example.com/research", "domain": "example.com", "snippet": "Search-result snippet...", "status": 200, "sourceType": "guide_or_article", "pageSummary": "Short page summary...", "evidence": ["Relevant sentence extracted from the page."], "relevanceScore": 0.67, "inspected": true, "error": "" } ], "topFindings": [ { "finding": "Relevant sentence extracted from the page.", "sourceUrl": "https://example.com/research", "sourceTitle": "Example source", "confidence": "medium" } ], "entities": ["agents", "apify", "mcp"], "risks": ["Treat findings as research leads with source URLs, not verified final facts"], "nextSearches": ["AI agents Apify actors MCP UK official documentation"] } ``` ### Common Downstream Workflows 1. Run this Actor with a market or competitor query. 2. Pass high-relevance `sourceRecords` to a crawler, summariser, SEO writer, or research agent. 3. Use `topFindings` as cited context seeds in a memo or brief. 4. Run `nextSearches` when `dataQuality` is `low` or the agent needs corroboration. ### What This Actor Cannot Guarantee Search engines can personalise, block, or reshape result pages. This Actor uses bounded public web search and lightweight page inspection, so it should be treated as a fast discovery layer rather than a final authority. ### Pricing And Runtime The Actor is designed for short agent loops. Keep `maxResults` and `maxPagesToInspect` low for fast runs, then increase them only when a workflow needs broader coverage. # Actor input Schema ## `query` (type: `string`): Search query to research. ## `market` (type: `string`): Optional market/location context to append to the query. ## `maxResults` (type: `integer`): Maximum search results to return. ## `maxPagesToInspect` (type: `integer`): Maximum result pages to fetch and inspect for summaries, evidence sentences, and relevance signals. Set to 0 to return SERP records only. ## Actor input object example ```json { "query": "AI agents Apify actors MCP", "market": "UK", "maxResults": 8, "maxPagesToInspect": 5 } ``` # Actor output Schema ## `results` (type: `string`): Structured results in the default dataset. # 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 = { "query": "AI agents Apify actors MCP", "market": "UK" }; // Run the Actor and wait for it to finish const run = await client.actor("iammienta/agent-serp-research-pack").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 = { "query": "AI agents Apify actors MCP", "market": "UK", } # Run the Actor and wait for it to finish run = client.actor("iammienta/agent-serp-research-pack").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 '{ "query": "AI agents Apify actors MCP", "market": "UK" }' | apify call iammienta/agent-serp-research-pack --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iammienta/agent-serp-research-pack", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SERP Research Pack for AI Agents", "description": "Turn a search query into an agent-ready SERP research brief. Returns source URLs, findings, extracted entities, risks, and suggested next searches for market research, SEO planning, content briefs, and tool discovery.", "version": "0.2", "x-build-id": "Rx9jYFgeRoAXBUBoh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iammienta~agent-serp-research-pack/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iammienta-agent-serp-research-pack", "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/iammienta~agent-serp-research-pack/runs": { "post": { "operationId": "runs-sync-iammienta-agent-serp-research-pack", "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/iammienta~agent-serp-research-pack/run-sync": { "post": { "operationId": "run-sync-iammienta-agent-serp-research-pack", "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": [ "query" ], "properties": { "query": { "title": "Research query", "type": "string", "description": "Search query to research." }, "market": { "title": "Market or location", "type": "string", "description": "Optional market/location context to append to the query." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum search results to return.", "default": 8 }, "maxPagesToInspect": { "title": "Max pages to inspect", "minimum": 0, "maximum": 10, "type": "integer", "description": "Maximum result pages to fetch and inspect for summaries, evidence sentences, and relevance signals. Set to 0 to return SERP records only.", "default": 5 } } }, "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 } } } } } } } } } } ```