# ClinicalTrials Intelligence MCP — 450K+ Studies (`andrew_avina/clinicaltrials-intelligence-mcp`) Actor Search, filter, and analyze 450,000+ clinical trials from ClinicalTrials.gov via a Model Context Protocol (MCP) server. This Apify Actor exposes structured clinical trial data to AI assistants (Claude, GPT-4, Cursor), research pipelines, and compliance systems — no API key or ... - **URL**: https://apify.com/andrew\_avina/clinicaltrials-intelligence-mcp.md - **Developed by:** [Andrew Avina](https://apify.com/andrew_avina) (community) - **Categories:** MCP servers, Business - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $3.00 / 1,000 result item returneds 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 ## ClinicalTrials Intelligence MCP — 450K+ Global Clinical Trials Search, filter, and analyze 450,000+ clinical trials from ClinicalTrials.gov via a Model Context Protocol (MCP) server. This Apify Actor exposes structured clinical trial data to AI assistants (Claude, GPT-4, Cursor), research pipelines, and compliance systems — no API key or authentication required. --- ### Who Uses This #### Pharmaceutical & Biotech Companies - **Competitive intelligence teams** monitoring rival drug development pipelines across indication areas - **Business development analysts** identifying partnership or licensing opportunities based on phase progression - **Clinical operations managers** benchmarking enrollment rates and trial duration by therapeutic area - **Medical affairs** teams tracking the landscape of approved vs. investigational therapies #### Investment & Finance - **Biotech analysts** screening Phase 2→3 transitions to assess near-term catalysts - **Hedge funds** with biotech exposure monitoring portfolio company trial status - **Venture capital** firms performing due diligence on clinical-stage companies - **Deal teams** assessing competitive moat from first-mover advantage in a therapeutic area #### Healthcare & Patient Advocacy - **Patient advocates** finding recruiting trials for rare disease communities - **Oncology navigators** matching patients to eligible clinical trials by diagnosis and location context - **Healthcare journalists** verifying drug development claims and trial status #### Academic Research - **Systematic review teams** collecting all trials in a therapeutic area - **Epidemiologists** studying research investment patterns by disease burden - **Grant writers** benchmarking their proposed work against existing active trials - **Biostatisticians** analyzing trial design trends (e.g. adaptive trials, Bayesian endpoints) --- ### What You Get Each trial record returned by this actor contains: | Field | Description | |-------|-------------| | `nct_id` | ClinicalTrials.gov identifier (e.g. NCT05123456) | | `title` | Trial brief title | | `status` | RECRUITING, COMPLETED, ACTIVE_NOT_RECRUITING, etc. | | `phase` | PHASE1, PHASE2, PHASE3, PHASE4, or NA | | `condition` | Primary medical condition being studied | | `intervention` | Primary drug/device/procedure being tested | | `sponsor` | Name of the lead sponsor organization | | `sponsor_type` | INDUSTRY, NIH, OTHER, or FED | | `start_date` | Trial start date (YYYY-MM or YYYY-MM-DD) | | `completion_date` | Estimated or actual completion date | | `enrollment` | Estimated or actual enrollment count | | `brief_summary` | First 500 characters of trial description | | `source` | Always `clinicaltrials.gov` | --- ### Quick Start #### Batch Mode (dataset output) Run the actor with standard input to collect trial data into a dataset: ```json { "condition": "type 2 diabetes", "status": "RECRUITING", "limit": 50 } ```` Results appear in the actor's default dataset, downloadable as JSON, CSV, or XLSX from the Apify Console. #### MCP Server Mode (AI assistant integration) Enable `serveMcp: true` to start the HTTP server on port 4321: ```json { "serveMcp": true } ``` Then configure your MCP client to point at `http://:4321`. **Claude Desktop config** (`~/Library/Application Support/Claude/claude_desktop_config.json`): ```json { "mcpServers": { "clinicaltrials": { "url": "http://your-apify-run-url:4321" } } } ``` *** ### MCP Tools Reference #### `search_trials` Search trials by condition, drug, keyword, status, phase, and sponsor type. **Parameters:** | Parameter | Type | Description | |-----------|------|-------------| | `condition` | string | Medical condition (e.g. "breast cancer", "Parkinson disease") | | `intervention` | string | Drug/device name (e.g. "pembrolizumab", "insulin glargine") | | `query` | string | General keyword search | | `status` | string | RECRUITING, COMPLETED, ANY, ACTIVE\_NOT\_RECRUITING, NOT\_YET\_RECRUITING, TERMINATED, WITHDRAWN | | `phase` | string | PHASE1, PHASE2, PHASE3, PHASE4, EARLY\_PHASE1, NA | | `sponsor_type` | string | INDUSTRY, NIH, OTHER, FED | | `limit` | integer | Max results (1–100, default 10) | **Example MCP call:** ```json { "name": "search_trials", "arguments": { "condition": "non-small cell lung cancer", "intervention": "pembrolizumab", "status": "RECRUITING", "phase": "PHASE3", "sponsor_type": "INDUSTRY", "limit": 20 } } ``` **Example response:** ```json [ { "nct_id": "NCT05432100", "title": "Pembrolizumab Plus Chemotherapy in NSCLC", "status": "RECRUITING", "phase": "PHASE3", "condition": "Non-Small Cell Lung Carcinoma", "intervention": "Pembrolizumab", "sponsor": "Merck Sharp & Dohme LLC", "sponsor_type": "INDUSTRY", "start_date": "2024-03", "completion_date": "2027-12", "enrollment": 450, "brief_summary": "A randomized, double-blind Phase 3 trial...", "source": "clinicaltrials.gov" } ] ``` *** #### `get_trial` Retrieve full structured details for a single trial by NCT ID. **Parameters:** | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `nct_id` | string | Yes | NCT identifier (e.g. "NCT05123456") | **Example MCP call:** ```json { "name": "get_trial", "arguments": { "nct_id": "NCT05432100" } } ``` *** #### `get_trial_stats` Get aggregate statistics for a condition or intervention: counts by enrollment status, phase, and sponsor type. Ideal for landscape analysis. **Parameters:** | Parameter | Type | Description | |-----------|------|-------------| | `condition` | string | Medical condition to analyze | | `intervention` | string | Drug or intervention to analyze | **Example MCP call:** ```json { "name": "get_trial_stats", "arguments": { "condition": "Alzheimer disease" } } ``` **Example response:** ```json { "total_count": 1847, "sample_size": 100, "by_status": { "RECRUITING": 42, "COMPLETED": 31, "ACTIVE_NOT_RECRUITING": 15, "NOT_YET_RECRUITING": 8, "TERMINATED": 4 }, "by_phase": { "PHASE2": 38, "PHASE3": 22, "PHASE1": 19, "PHASE4": 11, "NA": 10 }, "by_sponsor_type": { "INDUSTRY": 55, "OTHER": 30, "NIH": 15 }, "source": "clinicaltrials.gov" } ``` *** ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `condition` | string | `""` | Medical condition to search | | `intervention` | string | `""` | Drug or device name | | `query` | string | `""` | General keyword | | `nctId` | string | `""` | Single NCT ID lookup (bypasses other filters) | | `status` | string | `"ANY"` | Enrollment status filter | | `phase` | string | `""` | Clinical phase filter | | `sponsorType` | string | `""` | Sponsor organization class | | `limit` | integer | `20` | Max records (1–500) | | `serveMcp` | boolean | `false` | Start MCP HTTP server on port 4321 | *** ### API Details This actor queries the **ClinicalTrials.gov v2 REST API** (launched 2024): - **Base URL:** `https://clinicaltrials.gov/api/v2` - **Auth:** None required - **Rate limits:** None published; the actor uses a single connection per request - **Data freshness:** Updated daily by NLM/NIH - **Coverage:** 450,000+ trials from 220+ countries - **Data license:** Public domain (U.S. government work) The v2 API is significantly more structured than the deprecated v1 API, with properly nested JSON objects for identification, status, sponsors, eligibility, arms/interventions, and contacts. *** ### Common Use Cases #### Finding Recruiting Trials for a Specific Drug Combination ```json { "condition": "triple negative breast cancer", "intervention": "olaparib", "status": "RECRUITING", "limit": 30 } ``` #### Competitive Pipeline Analysis — All Industry Phase 3 Trials ```json { "condition": "heart failure", "phase": "PHASE3", "sponsorType": "INDUSTRY", "limit": 100 } ``` #### NIH-Funded Research in Rare Diseases ```json { "condition": "Duchenne muscular dystrophy", "sponsorType": "NIH", "limit": 50 } ``` #### Single Trial Deep Dive ```json { "nctId": "NCT04280705" } ``` #### Landscape Statistics for Grant Writing ```json { "condition": "pancreatic cancer", "serveMcp": false } ``` Then call `get_trial_stats` via the MCP interface. *** ### Data Quality & Caveats - **Trial registration is mandatory** for most interventional trials (per ICMJE requirements and FDA Amendments Act), so coverage of Phase 2–4 is high - **Observational studies** are included; filter by `phase=NA` to focus on them - **Data accuracy** depends on sponsor submissions; NLM performs quality checks but cannot verify all entries - **Phase field** may be missing for older entries or device trials - **Enrollment counts** may be estimates that differ from actual enrollment - **International trials** are included; the ClinicalTrials.gov database is the world's largest, though WHO ICTRP covers additional registries *** ### Comparison to Alternatives | Source | Coverage | Auth | Cost | Structured API | |--------|----------|------|------|----------------| | **ClinicalTrials.gov v2** (this actor) | 450K+ trials worldwide | None | Free | Yes (v2) | | clinicaltrials.gov v1 (deprecated) | Same | None | Free | Limited | | WHO ICTRP | 850K+ (includes 17 registries) | None | Free | Partial | | Citeline/Pharma Intelligence | Curated, enriched | Subscription ~$50K/yr | Paid | Yes | | GlobalData | Curated, enriched | Subscription ~$30K/yr | Paid | Yes | | Medidata/Veeva Vault | Operational data | Customer only | Enterprise | Yes | This actor gives you free, structured, real-time access to the largest single clinical trial registry in the world — the same data that powers clinicaltrials.gov's search interface. *** ### Error Handling The actor is designed to never crash. On API failures: ```json [ { "_meta": { "error": "Connection timeout after 30s", "fallback_tried": true } } ] ``` On empty results: ```json [ { "nct_id": "", "title": "No results found", "status": "", "sponsor": "", "source": "clinicaltrials.gov", "_meta": { "query": {"condition": "xyzxyz123", "status": "ANY"}, "result_count": 0 } } ] ``` *** ### Pricing Context This actor uses the ClinicalTrials.gov v2 API which is completely free and public domain. The Apify actor runtime cost is billed per compute unit (CU). A typical batch query of 50 trials uses approximately 0.01–0.05 CUs. MCP server mode uses roughly 0.1 CUs/hour while idle. For high-volume commercial use cases (pulling 50,000+ trials or running 24/7 MCP server mode), consider Apify's business or enterprise plans. *** ### Support & Feedback - **Issues:** File issues via the Apify Console actor page - **API docs:** https://clinicaltrials.gov/data-api/api - **MCP spec:** https://modelcontextprotocol.io/ - **Data source:** https://clinicaltrials.gov This actor is built and maintained as part of the AeternaOS SECC platform — sovereign executive command infrastructure for AI-native businesses. # Actor input Schema ## `condition` (type: `string`): Medical condition or disease to search for. Examples: 'diabetes', 'lung cancer', 'Alzheimer disease', 'multiple sclerosis', 'COVID-19'. Maps to ClinicalTrials.gov query.cond parameter. ## `intervention` (type: `string`): Drug, device, or therapeutic intervention name. Examples: 'semaglutide', 'pembrolizumab', 'CAR-T', 'mRNA vaccine'. Maps to query.intr parameter. ## `query` (type: `string`): Free-text keyword search across all trial fields including title, summary, sponsor, conditions, and interventions. Use when condition/intervention are not sufficient. ## `nctId` (type: `string`): ClinicalTrials.gov identifier for a specific trial (e.g. NCT05123456). When provided, fetches only that single trial and ignores other filters. ## `status` (type: `string`): Filter trials by current enrollment status. RECRUITING = actively enrolling patients. COMPLETED = finished. ACTIVE\_NOT\_RECRUITING = ongoing but closed to new enrollment. ANY = no filter. ## `phase` (type: `string`): Clinical development phase. PHASE1=safety/dose, PHASE2=efficacy, PHASE3=confirmatory large-scale, PHASE4=post-market. Leave empty for all phases. ## `sponsorType` (type: `string`): Filter by the type of organization leading the trial. INDUSTRY=pharma/biotech companies, NIH=National Institutes of Health, OTHER=academic/non-profit/hospital. Leave empty for all. ## `limit` (type: `integer`): Maximum number of trial records to return. In MCP server mode, this is ignored and all queries are made live. In batch mode, max is 500. ## `serveMcp` (type: `boolean`): When enabled, starts an MCP-compatible HTTP server on port 4321 instead of returning a batch dataset. Use this to connect Claude Desktop, Cursor, or any MCP client directly to live ClinicalTrials.gov data. The actor runs continuously until stopped. ## Actor input object example ```json { "condition": "", "intervention": "", "query": "", "nctId": "", "status": "ANY", "phase": "", "sponsorType": "", "limit": 20, "serveMcp": false } ``` # 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("andrew_avina/clinicaltrials-intelligence-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("andrew_avina/clinicaltrials-intelligence-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 andrew_avina/clinicaltrials-intelligence-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=andrew_avina/clinicaltrials-intelligence-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ClinicalTrials Intelligence MCP — 450K+ Studies", "description": "Search, filter, and analyze 450,000+ clinical trials from ClinicalTrials.gov via a Model Context Protocol (MCP) server. This Apify Actor exposes structured clinical trial data to AI assistants (Claude, GPT-4, Cursor), research pipelines, and compliance systems — no API key or ...", "version": "0.1", "x-build-id": "OIL17gdcE0GMhHcgN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/andrew_avina~clinicaltrials-intelligence-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-andrew_avina-clinicaltrials-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/andrew_avina~clinicaltrials-intelligence-mcp/runs": { "post": { "operationId": "runs-sync-andrew_avina-clinicaltrials-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/andrew_avina~clinicaltrials-intelligence-mcp/run-sync": { "post": { "operationId": "run-sync-andrew_avina-clinicaltrials-intelligence-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "condition": { "title": "Medical Condition", "type": "string", "description": "Medical condition or disease to search for. Examples: 'diabetes', 'lung cancer', 'Alzheimer disease', 'multiple sclerosis', 'COVID-19'. Maps to ClinicalTrials.gov query.cond parameter.", "default": "" }, "intervention": { "title": "Drug / Intervention", "type": "string", "description": "Drug, device, or therapeutic intervention name. Examples: 'semaglutide', 'pembrolizumab', 'CAR-T', 'mRNA vaccine'. Maps to query.intr parameter.", "default": "" }, "query": { "title": "General Keyword Search", "type": "string", "description": "Free-text keyword search across all trial fields including title, summary, sponsor, conditions, and interventions. Use when condition/intervention are not sufficient.", "default": "" }, "nctId": { "title": "NCT ID (single trial lookup)", "type": "string", "description": "ClinicalTrials.gov identifier for a specific trial (e.g. NCT05123456). When provided, fetches only that single trial and ignores other filters.", "default": "" }, "status": { "title": "Enrollment Status", "enum": [ "ANY", "RECRUITING", "COMPLETED", "ACTIVE_NOT_RECRUITING", "NOT_YET_RECRUITING", "TERMINATED", "WITHDRAWN" ], "type": "string", "description": "Filter trials by current enrollment status. RECRUITING = actively enrolling patients. COMPLETED = finished. ACTIVE_NOT_RECRUITING = ongoing but closed to new enrollment. ANY = no filter.", "default": "ANY" }, "phase": { "title": "Trial Phase", "enum": [ "", "PHASE1", "PHASE2", "PHASE3", "PHASE4", "EARLY_PHASE1", "NA" ], "type": "string", "description": "Clinical development phase. PHASE1=safety/dose, PHASE2=efficacy, PHASE3=confirmatory large-scale, PHASE4=post-market. Leave empty for all phases.", "default": "" }, "sponsorType": { "title": "Sponsor Type", "enum": [ "", "INDUSTRY", "NIH", "OTHER", "FED" ], "type": "string", "description": "Filter by the type of organization leading the trial. INDUSTRY=pharma/biotech companies, NIH=National Institutes of Health, OTHER=academic/non-profit/hospital. Leave empty for all.", "default": "" }, "limit": { "title": "Result Limit", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of trial records to return. In MCP server mode, this is ignored and all queries are made live. In batch mode, max is 500.", "default": 20 }, "serveMcp": { "title": "MCP Server Mode", "type": "boolean", "description": "When enabled, starts an MCP-compatible HTTP server on port 4321 instead of returning a batch dataset. Use this to connect Claude Desktop, Cursor, or any MCP client directly to live ClinicalTrials.gov data. The actor runs continuously until stopped.", "default": false } } }, "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 } } } } } } } } } } ```