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Latest snapshot2026-09-12USmethodology registry-public-v1
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119,491 tools
- Accountget · account
Information about the account associated with the API key: plan, credit balance, and the key's metadata. Unchanged from the legacy API (response uses the legacy `request_status` format).RiveterRiveter
PluginrequiredAI - Build configured datasetbuild · build configured dataset
Run a **configured dataset** — a reusable, pre-configured dataset template (id `cds_...`) set up for your account. The spec, prompt template, row cap, and per-run credit cost are all defined on the template; you only supply `parameters` to fill in its `{{ placeholder }}` values.
Returns the dataset-build run; poll [GET /runs/{id}](#tag/runs/get/runs/{id}).
**Example — Fill in template parameters:**
{
"parameters": {
"practice_type": "dentists",
"state": "ohio"
}
}
**Workflow:** Starts an async run of a configured dataset. Poll `get_run`, then fetch with `get_run_result`.RiveterRiveterPluginrequiredAI
- Build datasetbuild · build dataset
Build a dataset from a natural-language prompt, a structured spec, or both. Riveter finds the rows for you.
- **Prompt only** — e.g. "top 50 US SaaS companies with their websites". The builder analyzes the prompt into identifiers/qualifiers/attributes automatically.
- **Structured spec** — `identifiers` (what each row is, e.g. "Company name") and `qualifiers` (constraints rows must satisfy). Optional `attributes` are saved as the enrichment columns described above.
- **Both** — the prompt is combined with the spec.
**Note**: The result contains the identifier columns only (e.g. company name + website). `attributes` are not immediately filled in by the build — they become the output columns of an enrichment that can run later, as a separate step. To get attributes filled: pass `auto_run_enrichment: true`, or call [POST /enrich](#tag/enrich/post/enrich) with `dataset_id` once the build completes. This is a second paid run: `dry_run` shows the combined estimate.
Returns the dataset-build run; poll [GET /runs/{id}](#tag/runs/get/runs/{id}) and fetch rows with [GET /runs/{id}/result](#tag/runs/get/runs/{id}/result), or pass `dataset_webhook_url`.
**Example — Prompt with identifiers and qualifiers:**
{
"prompt": "Top 50 US SaaS companies with their websites",
"identifiers": [
"Company name",
"Website"
],
"qualifiers": [
"US-based",
"SaaS"
],
"max_items": 50
}
**Example — Refuse the build if it could cost more than 200 credits:**
{
"prompt": "Top 50 US SaaS companies with their websites",
"max_items": 50,
"max_credits": 200
}
**Example — Structured spec:**
{
"identifiers": [
"Company name",
"Website"
],
"qualifiers": [
"US-based",
"SaaS"
],
"attributes": [
"CEO",
"Employee count"
],
"max_items": 50
}
**Example — Build and auto-enrich in one step:**
{
"prompt": "Top 50 US SaaS companies",
"attributes": [
"CEO",
"Revenue"
],
"max_items": 50,
"auto_run_enrichment": true,
"auto_run_enrichment_webhook_url": "https://your-server.com/webhook"
}
**Workflow:** Starts an async dataset build run. Poll `get_run` until status is 'success', then fetch the rows with `get_run_result`. Pass `auto_run_enrichment: true` to enrich the rows in the same flow.RiveterRiveterPluginrequiredAI
- Create enrichmentcreate · create enrichment
Create an enrichment (no run) from a **completed dataset build**: the dataset's rows become the enrichment's input rows, and its attributes become output columns. Configure further in the UI or via [PATCH /enrichments/{id}](#tag/enrich/patch/enrichments/{id}), then run with [POST /enrich](#tag/enrich/post/enrich).RiveterRiveterPluginrequiredAI
- Create extractioncreate · create extraction
Create an **extraction** — a reusable recipe for scraping structured records from a website — and start its agent discovery. Discovery explores the site, builds the scrape/extract plan, and validates it against your schema.
Poll [GET /extractions/{id}](#tag/extractions/get/extractions/{id}) until `status` is `ready`, then execute it with [POST /extractions/{id}/runs](#tag/extractions/post/extractions/{id}/runs). The extraction (`ext_...`) and its runs (`run_...`) are different resources.
Creating an extraction charges discovery credits (returned as `credits_charged`). To see the price first, send `dry_run: true`.
**Example — Create an extraction:**
{
"starting_url": "https://example.com/products",
"goal_description": "Extract every product with its name and price",
"output_record_json_schema": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"price": {
"type": "string"
}
}
},
"required_keys": [
"name"
]
}
**Workflow:** Discovery is async but returns no run — poll `get_extraction` until `status` is 'ready', then execute with `run_extraction`.RiveterRiveterPluginrequiredAI
- Create monitorcreate · create monitor
Create a **monitor**: a schedule that re-runs an enrichment daily, weekly, or monthly and can POST results (or only changes) to a webhook.
**Example — Daily monitor with change alerts:**
{
"enrichment_id": "enr_018f5b60-1234-7abc-89ab-0123456789ab",
"cadence": "daily",
"hour": 9,
"minute": 0,
"timezone": "UTC",
"alert_rule": "only_on_change",
"webhook_url": "https://your-server.com/webhook"
}
**Workflow:** Creates a scheduled monitor. Check it with `get_monitor`, pause it with `update_monitor` (enabled: false), and list its runs with `list_monitor_runs`.RiveterRiveterPluginrequiredAI
- Enrichenrich · enrich
Add AI-researched columns to rows. Rows come from exactly one of `input` (columnar: `{ "Header": ["row1", "row2"] }`, all arrays equal length) or `dataset_id` (a completed build, `ds_...`).
Column config, exactly one of, in order of preference:
1. `enrichment_id` — run a saved enrichment (`enr_...`, from `list_enrichments`). Fixed, tested config; most consistent. Up to 10,000 rows. Input headers must match the enrichment's input columns (`get_enrichment`).
2. `prompt` + `attributes` — Riveter drafts the column config from a description and up to 20 output column names. No setup; results vary per run. Up to 1,000 rows.
3. `output` — full per-column spec: `prompt`, `contexts`, `tools`, `format`, `run_when`, or a tool-only `tool`. Up to 1,000 rows. Call `help` with `column_config`, `run_when`, `formats`, `tools`, or `examples` before writing one.
Returns a run. Poll `get_run_result` with `wait: 50` (or pass `webhook_url`). `dry_run: true` returns the credit estimate without running.
**Example — Run a saved enrichment:**
{
"enrichment_id": "enr_018f5b60-1234-7abc-89ab-0123456789ab",
"input": {
"Company Name": [
"Acme Corp",
"Tech Solutions Inc"
]
}
}
**Example — Prompt + attributes (auto-generated config):**
{
"prompt": "Research each company",
"attributes": [
"CEO",
"Employee Count",
"Industry"
],
"input": {
"Company": [
"Apple",
"Google"
]
},
"webhook_url": "https://your-server.com/webhook"
}
**Example — Full output specification:**
{
"output": {
"Employee Count": {
"prompt": "Find the number of employees at this company",
"contexts": [
"Company"
],
"format": "number"
}
},
"input": {
"Company": [
"Apple",
"Google"
]
}
}
**Example — Enrich the rows of a completed dataset build:**
{
"enrichment_id": "enr_018f5b60-1234-7abc-89ab-0123456789ab",
"dataset_id": "ds_018f6a70-1234-7abc-89ab-0123456789ab"
}
**Example — Price the run without starting it:**
{
"enrichment_id": "enr_018f5b60-1234-7abc-89ab-0123456789ab",
"input": {
"Company Name": [
"Acme Corp",
"Tech Solutions Inc"
]
},
"dry_run": true
}
**Workflow:** Starts an async run. Poll `get_run` with the returned run id until status is 'success', then call `get_run_result` for the output.RiveterRiveterPluginrequiredAI
- Enrichment → datasetbuild · enrichment → dataset
Build a dataset **shaped for this enrichment**: identifiers are derived from the enrichment's source-data columns automatically, so generated rows land as valid input rows. The build finds rows only; the enrichment's output columns are not researched until the enrichment runs.
Optionally set `auto_run_enrichment: true` to run the enrichment on the rows as soon as the build completes (a second paid run; the kickoff response then carries `enrichment_run_id`).
Returns the dataset-build run — poll it via [GET /runs/{id}](#tag/runs/get/runs/{id}).
**Example — Build rows and auto-run the enrichment:**
{
"prompt": "US-based fintech startups",
"qualifiers": [
"B2B",
"founded after 2015"
],
"max_items": 100,
"auto_run_enrichment": true
}
**Workflow:** Starts an async dataset build run matching the enrichment's input columns. Poll `get_run`, then fetch rows with `get_run_result`.RiveterRiveterPluginrequiredAI
- Extend datasetextend · extend dataset
Generate **new rows** for an existing completed dataset build. The new rows are deduplicated against the source build's rows. Identifiers and attributes are inherited from the source and cannot be overridden; `qualifiers` and `max_items` may be replaced, and an optional `prompt` adds a new instruction.
Like [POST /datasets](#tag/datasets/post/datasets), this finds rows only (identifier columns). Inherited attributes are not researched; they are filled by `auto_run_enrichment: true` or a later [POST /enrich](#tag/enrich/post/enrich) with the new `dataset_id`.
Returns a fresh dataset-build run (the source build is untouched).
**Workflow:** Starts an async run that adds rows to an existing dataset. Poll `get_run`, then fetch with `get_run_result`.RiveterRiveterPluginrequiredAI
- Get extractionget · get extraction
The extraction's status and definition. `status` is `discovering` while the agent builds the plan, then `ready` (or `discovery_failed`). Once `ready`, execute with [POST /extractions/{id}/runs](#tag/extractions/post/extractions/{id}/runs).RiveterRiveterPluginrequiredAI
- Get monitorget · get monitor
The monitor's schedule, webhook, and next run time.RiveterRiveterPluginrequiredAI
- List enrichmentslist · list enrichments
List the account's enrichments, most recently updated first. Each item is compact — id, name, status, timestamps, and column names/count — so the list stays small even for accounts with thousands of enrichments. Column configuration (prompts, tools, formats) is on [GET /enrichments/{id}](#tag/enrich/get/enrichments/{id}).
Filter by `status` (comma-separated) and `name` (case-insensitive substring). Paginate with `page` / `per_page` (max 50). Column names are capped at 40 per input/output list; when cut, `column_names_truncated` is `true` and `column_count` still reports the total.RiveterRiveterPluginrequiredAI
- List enrichments (summary)enrichments · list enrichments (summary)
All-time enrichment counts by status — how many of the account's enrichments are pending, running, finished, or stopped. For the enrichments themselves use [GET /enrichments](#tag/enrich/get/enrichments)`?status=...`.RiveterRiveterPluginrequiredAI
- List monitor runslist · list monitor runs
The monitor's run history, newest first. Fetch a specific run's data with [GET /runs/{id}/result](#tag/runs/get/runs/{id}/result). Supports `status`, `page`, and `per_page`.RiveterRiveterPluginrequiredAI
- List monitorslist · list monitors
List the account's monitors, newest first.RiveterRiveterPluginrequiredAI
- List runslist · list runs
List the account's runs, newest first. Every async operation shows up here — enrichment runs, dataset builds, extractions, and quick searches.
Filter by `type` (comma-separated), `status`, `enrichment_id`, `monitor_id`, and `created_after` / `created_before` (ISO 8601). Paginate with `page` / `per_page`.RiveterRiveterPluginrequiredAI
- List runs (summary)runs · list runs (summary)
All-time run counts by status — a snapshot of the account's run queue. For the runs themselves use [GET /runs](#tag/runs/get/runs)`?status=...`.RiveterRiveterPluginrequiredAI
- Quick searchquick · quick search
Run a web search and get structured results back **synchronously** — the response is the run with the results already in `output` (no polling, no webhook). Optionally filter to a date range with `date_start` / `date_end` (format `YYYY-MM-DD`); if only `date_start` is given, `date_end` defaults to today.
The result is also stored on the run, so it stays re-fetchable at [GET /runs/{id}/result](#tag/runs/get/runs/{id}/result).
For bulk searches — up to **100,000** per request, async with `webhook_url` support — use the legacy [POST /v1/web_search](./openapi.legacy.yaml) endpoint.
## Quick example
```bash
curl -X POST https://api.riveterhq.com/v1/quick_search \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "latest OpenAI news"}'
```
## Credit costs
- **0.04 credits** per search.
**Example — A simple search:**
{
"query": "latest OpenAI news"
}
**Example — A search filtered to a date range:**
{
"query": "OpenAI GPT-4o mini",
"date_start": "2024-07-01",
"date_end": "2024-07-31"
}RiveterRiveterPluginrequiredAI
- Read enrichmentget · read enrichment
The enrichment's structure: its input (source-data) columns and full output column configuration — the same shape you would send to [POST /enrich](#tag/enrich/post/enrich) as `output`.RiveterRiveterPluginrequiredAI
- Run extractionrun · run extraction
Execute a `ready` extraction. Returns a run — poll [GET /runs/{id}](#tag/runs/get/runs/{id}) and fetch the extracted records with [GET /runs/{id}/result](#tag/runs/get/runs/{id}/result) (the records come back as an array of JSON objects matching your schema), or pass a `webhook_url`.
`variables` fills any `{{ placeholder }}` values the plan defines (e.g. a search term or location). Each run charges run credits (`credits_charged`).
**Workflow:** Starts an async extraction run. Poll `get_run`, then fetch the records with `get_run_result`.RiveterRiveterPluginrequiredAI
- Run resultget · run result
The run plus `output`. `output` is `null` until the run reaches a terminal state..
Pass `?wait=N` (max 50) to long-poll: the request holds until the run finishes or the budget elapses, whichever comes first.
## Output shape by run type
- **enrichment** — an object mapping column headers to arrays of cell objects: `{ "Company": [{"value": "Apple"}], "CEO": [{"value": "Tim Cook"}] }`
- **dataset_build** — an object mapping column headers to arrays of cell objects (same columnar shape as enrichment)
- **extraction** — the extracted records as an array of JSON objects (matching your `output_record_json_schema`)
- **quick_search** — the search result object `{ "results": [{ "title", "link", "snippet" }, ...], "knowledge_graph"? }`, the same data the synchronous `POST /quick_search` response already carried. (Runs started on the legacy async `/web_search` endpoint share this run type but return the columnar enrichment shape with a `search_results` column.)
- **search_agent** — the answer object `{ "result": <string or object> }`; `result` matches the request's `output_schema` when one was given, otherwise it's free text.
**Workflow:** `output` is null until the run finishes. Pass `wait` (seconds, see the parameter's max) to long-poll instead of sleeping between calls.RiveterRiveterPluginrequiredAI
- Run statusget · run status
This returns the **status and progress** of a run for any run created from enrichments, datasets, or extractions. `status` reports the status of the run, and `progress` gives a completion estimate.
**Workflow:** Poll this endpoint until `status` is 'success' or 'stopped'. Typical runs take 10-120 seconds. Poll every 5-10 seconds.RiveterRiveterPluginrequiredAI
- Scrapescrape · scrape
Scrape a webpage and return the text content **synchronously** — the only endpoint here that doesn't return a run to poll. Unchanged from the legacy API (response uses the legacy `request_status` format).
## Quick example
```bash
curl -X POST https://api.riveterhq.com/v1/scrape \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com"}'
```
## Credit costs
- **With proxy**: 1/5 credit (0.20 credits)
- **Without proxy**: 1/20 credit (0.05 credits)
- **From cache**: 1/100 credit (0.01 credits)
## Proxy usage
Scraping is not guaranteed to succeed without a proxy. Some websites may block requests or require specific geographic locations. To use a proxy, include `proxy_country_code` with a two-character country code (e.g. 'us', 'gb', 'de').
## Caching
Recently scraped pages are cached to save credits (0.01 credits on a cache hit). Set `skip_cache: true` to always fetch fresh content.RiveterRiveterPluginrequiredAI
- Search agentsearch · search agent
Ask a question and get an answer. This is meant for quick, relatively scoped one-off questions, like "What is the NAICS code for this company...". For more complex questions, use the `enrich/` endpoint.
This uses the same AI + web-tool loop that fills a single agent-mode cell in an enrichment (web search, web scraping, PDF reading, HTTP requests), with no enrichment setup.
The run is processed in the background while this request **long-polls up to `wait` seconds (default 50)** — most runs finish in time and return the answer inline in `output.result`. If the agent is still working when the budget elapses, the response comes back with `status: processing` and `output: null`; poll [GET /runs/{id}/result](#tag/runs/get/runs/{id}/result) (it long-polls too) until the run is terminal.
Pass `output_schema` (a JSON Schema object) to get `output.result` back as a structured object matching your schema instead of free text. Unanswerable questions return the string `"not found"`.
## Quick example
```bash
curl -X POST https://api.riveterhq.com/v1/search_agent \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Who is the current CEO of Anthropic, and when did they take the role?"}'
```
## Credit costs
- **1 credit** per call (same as one agent-mode enrichment cell), charged when the agent completes. Failed runs are not charged.
**Example — A free-text question:**
{
"prompt": "Who is the current CEO of Anthropic, and when did they take the role?"
}
**Example — A structured answer via output_schema:**
{
"prompt": "Find the founding year and headquarters city of Anthropic",
"output_schema": {
"type": "object",
"properties": {
"founding_year": {
"type": "integer"
},
"headquarters_city": {
"type": "string"
}
}
}
}RiveterRiveterPluginrequiredAI
- Stop runstop · stop run
Stop a run early. Works for every run type. Already-finished runs are left untouched; the response is the run either way.
**Workflow:** Stops a running run. The run cannot be resumed after stopping.RiveterRiveterPluginrequiredAI
- Update enrichmentupdate · update enrichment
Add, update, rename, or delete output columns, or reorder columns. Send the column changes keyed by column header inside `output` (recommended); a `column_order` array reorders columns.
Existing columns can be partially updated; new column names must include a full configuration; set `"delete": true` on a column to remove it. Columns use the same fields as the `output` spec on [POST /enrich](#tag/enrich/post/enrich).
**Example — Add a column:**
{
"output": {
"CEO": {
"prompt": "Find the company's CEO",
"contexts": [
"Company Name"
],
"format": "text"
}
}
}
**Example — Delete a column:**
{
"output": {
"Old Column": {
"delete": true
}
}
}RiveterRiveterPluginrequiredAI
- Update monitorupdate · update monitor
Pause, resume, or repoint a monitor. `enabled: false` pauses, `enabled: true` resumes; `webhook_url` replaces the delivery URL.
**Example — Pause:**
{
"enabled": false
}
**Example — Resume:**
{
"enabled": true
}RiveterRiveterPluginrequiredAI
- helphelp · Reference docs for using Riveter well. Call before writing an `output` column spec or when an error mentions a limit. Topics: overview, enrich_modes, column_config, run_when, formats, tools, examples, credits, datasets_vs_enrichment, limits, webhooks, errors.RiveterRiveterPluginrequiredAI
- Convert Page to Markdownpage · Fetch a web page URL and convert it to clean Markdown optimized for LLM context. Preserves headings, code blocks (with language hints), links, and tables; strips ads, navigation, and cookie banners. For Mintlify docs (docs.anthropic.com, OpenAI platform, Vercel, Stripe, etc.) tries the `URL.md` convention first for cleanest output. Discovers tab groups statically and emits each panel as a `### Tab: {label}` section instead of concatenating (prevents Python+TypeScript examples merging into one broken block). Zero external API calls — parses locally via linkedom.Page2AIPage2AIPluginnoneAI
- Adapt planadapt · Adapts an existing program for a user profile. Use when the user wants to customize a program.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Adapt workoutadapt · Adapts an existing workout for a user profile. Use when the user wants to customize a workout (duration, difficulty, equipment) without generating from scratch.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Clone stock programsclone · Clones stock programs to the user's team. Use when the user wants to add publisher programs to their library.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create dropcreate · Creates a social drop from an exercise, exercise group, workout, or program. Use when the user wants to create social media content from existing content.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create exercise groupcreate · Creates an exercise group (circuit or sets). Use when the user wants to group exercises for circuits or micro-workouts. Requires at least 2 exercise IDs.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create exercise instancecreate · Creates an exercise instance with duration/reps, pace, weight, RPE. Use when building a workout manually - create instances for each exercise, then pass instance IDs to create_workout. Stock and team exercises can be used directly via exercise_id.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create programcreate · Creates a workout program from team workouts. Use when the user wants to organize workouts into a multi-week program. Requires goal_ids (1-2) and workout_ids (min 2).Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create rest instancecreate · Creates a rest/break instance for use between exercises in a workout. Use when building a workout manually - create rest instances, then include their IDs in create_workout exercise_map. Workout cannot start or end with rest.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Create workoutcreate · Creates a workout from exercise and rest instances. Use when the user wants to build a custom workout. Provide either exercise_map (ordered array of instance IDs from create_exercise_instance and create_rest_instance) or instances (raw instance objects). Workout cannot start or end with rest.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Export dropexport · Exports drop assets for a channel (video URL, cover URL, copy pack). Use when the user wants to download assets or prepare a drop for external publishing.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Generate programgenerate · Generates an AI training program using team workouts. Use when the user wants to create a multi-week program from their workout library.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Generate video clipgenerate · Generates an AI exercise video from a trainer photo (CloneMotion). Use when the user wants to create exercise videos without filming. Before calling, ensure you have the target exercise and image source (upload URL or saved trainer image ID); optionally ask for specific video requirements. Accepts trainer_image_url (HTTP URL or data:image/... base64) or trainer_image_id from Workout Defaults.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Generate workoutgenerate · Generates an AI workout via Content API. Use when the user wants to create a personalized workout on-the-fly. Returns workout structure (not persisted to team library by default).Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get API usage statsget · Returns API key usage statistics for the current user's team. Use when the user asks about API usage, request counts, or rate limits.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get dashboard summaryget · Returns dashboard summary: latest created workouts with stats and ready-to-download items. Use when the user asks about dashboard, recent workouts, or content ready for download.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get embed code samplesget · Returns static copy-paste code templates for embed iframes, custom player JavaScript, or Content API cURL snippets. All samples contain only placeholder values (YOUR_ORG_ID, YOUR_API_KEY) — no real credentials are fetched or transmitted. Defaults sample_type to embed_prebuilt when omitted. Primary tool when the user asks for embed code, iframe HTML, or API snippet examples. Not for integration strategy or step-by-step plans (use get_integration_plan).Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get integration planget · Returns an integration strategy and step-by-step plan for Hyperhuman workouts: embed (iframe), stream videos (VOD export), or custom player (API + HLS). Use when the user asks how to integrate Hyperhuman or what integration options exist. Pass platform (web/mobile/hardware/cms) and use_case (mvp, ai_generated, corporate_wellness, vod, etc.). Not for copy-paste embed HTML snippets (use get_embed_code_samples for those).Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get session summaryget · Returns team name, plan label, and usage limits for the connected account. Use only when the user explicitly asks about their team, plan, or usage quotas.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get team exerciseget · Returns full details of an exercise from the team library. Use when the user selects an exercise or asks for exercise details.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get team programget · Returns full details of a workout program from the team. Use when the user selects a program or asks for program structure.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
- Get team usageget · Returns usage quotas for the authenticated team (e.g. social drop export count and limits). Use when the user asks about usage, quotas, or limits.Hyperhuman Agent WorkspaceHyperhumanPluginoptionalHealth & Wellness
What is Tool Explorer?
Tool Explorer indexes the callable tool names and descriptions attached to public registry profiles. It is useful for seeing what agents can actually invoke, not just which profile exists.
How do category and verb filters work?
Category filters use the live registry category rollup. Verb filters use the public tool insights rollup, so the page stays backed by the same read models as the tracker charts.
Why do auth requirements matter?
Auth requirements show whether a tool is likely usable without account connection, requires authentication, is private, or is unknown in the current snapshot.