# Secant Agent Research Pack (`secant_outreach/secant-agent-research-pack`) Actor MCP-first paid web research for autonomous agents: search, page extraction, JSON results, citations, and x402/Base USDC payment discovery. - **URL**: https://apify.com/secant\_outreach/secant-agent-research-pack.md - **Developed by:** [Carl Woods](https://apify.com/secant_outreach) (community) - **Categories:** AI, Agents, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% 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 ## Secant Agent Research Pack Actor This Actor exposes the Secant Agent Research Pack inside Apify while keeping the native x402 payment path canonical. Use it to discover payment requirements or to forward a valid x402 payment proof to the live API: - Research Pack: search + selected page extraction + citations for $0.012. - Search Only: ranked web search for $0.007. - Page Extract: readable text and metadata extraction for $0.001 per page. - Diff Monitor: page change checks for $0.003 per checked URL. ### Payment behavior If you omit `payment`, the Actor returns the upstream HTTP `402` response and the exact Base USDC payment requirement. If you include a valid x402 proof, the Actor forwards the request and writes the paid result to the dataset. This Actor does not custody funds and does not replace the native x402 flow. Payments settle directly to the configured Secant operator wallet. ### Example input ```json { "service": "research-pack", "query": "best AI agent data enrichment APIs", "maxResults": 5, "extractPages": 1 } ```` ### Output Each run writes one dataset item with: - `ok`: whether the upstream call returned `2xx`. - `status`: upstream HTTP status. - `service`: selected service. - `result`: upstream JSON response when available. - `paymentRequired`: true when the upstream service returned `402`. # Actor input Schema ## `service` (type: `string`): Paid service to call. Calls without payment proof return the x402 payment requirement. ## `query` (type: `string`): Search query for research-pack or search. ## `maxResults` (type: `integer`): Maximum ranked results for search or research-pack. ## `extractPages` (type: `integer`): Number of top pages to extract for research-pack. ## `urls` (type: `array`): URLs for extract or monitor-diff. ## `previousHash` (type: `string`): Previous content hash for monitor-diff. ## `previousText` (type: `string`): Previous page text for monitor-diff. ## `payment` (type: `object`): Optional x402 payment proof. Omit it to discover the 402 payment requirements. ## `paymentIdentifier` (type: `string`): Optional retry-safe payment identifier. ## `apiBaseUrl` (type: `string`): Secant Agent Research API base URL. ## Actor input object example ```json { "service": "research-pack", "query": "best AI agent data enrichment APIs", "maxResults": 5, "extractPages": 1, "apiBaseUrl": "https://agentic.secantoutreach.com" } ``` # 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": "best AI agent data enrichment APIs" }; // Run the Actor and wait for it to finish const run = await client.actor("secant_outreach/secant-agent-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": "best AI agent data enrichment APIs" } # Run the Actor and wait for it to finish run = client.actor("secant_outreach/secant-agent-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": "best AI agent data enrichment APIs" }' | apify call secant_outreach/secant-agent-research-pack --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=secant_outreach/secant-agent-research-pack", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Secant Agent Research Pack", "description": "MCP-first paid web research for autonomous agents: search, page extraction, JSON results, citations, and x402/Base USDC payment discovery.", "version": "0.0", "x-build-id": "Ri93blnpq4kIxqayt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/secant_outreach~secant-agent-research-pack/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-secant_outreach-secant-agent-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/secant_outreach~secant-agent-research-pack/runs": { "post": { "operationId": "runs-sync-secant_outreach-secant-agent-research-pack", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/secant_outreach~secant-agent-research-pack/run-sync": { "post": { "operationId": "run-sync-secant_outreach-secant-agent-research-pack", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "service" ], "properties": { "service": { "title": "Service", "enum": [ "research-pack", "search", "extract", "monitor-diff" ], "type": "string", "description": "Paid service to call. Calls without payment proof return the x402 payment requirement.", "default": "research-pack" }, "query": { "title": "Search query", "type": "string", "description": "Search query for research-pack or search." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 10, "type": "integer", "description": "Maximum ranked results for search or research-pack.", "default": 5 }, "extractPages": { "title": "Pages to extract", "minimum": 1, "maximum": 5, "type": "integer", "description": "Number of top pages to extract for research-pack.", "default": 1 }, "urls": { "title": "URLs", "type": "array", "description": "URLs for extract or monitor-diff.", "items": { "type": "string" } }, "previousHash": { "title": "Previous hash", "type": "string", "description": "Previous content hash for monitor-diff." }, "previousText": { "title": "Previous text", "type": "string", "description": "Previous page text for monitor-diff." }, "payment": { "title": "x402 payment proof", "type": "object", "description": "Optional x402 payment proof. Omit it to discover the 402 payment requirements." }, "paymentIdentifier": { "title": "Payment identifier", "type": "string", "description": "Optional retry-safe payment identifier." }, "apiBaseUrl": { "title": "API base URL", "type": "string", "description": "Secant Agent Research API base URL.", "default": "https://agentic.secantoutreach.com" } } }, "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 } } } } } } } } } } ```