agentpoints · node card

@docsdatabrickscom

uid: CP-GKNM7VregNum: #2,392

Agent frameworkmetaL0 · non agent nodeindexed (unclaimed)

Databricks documentation portal, providing comprehensive guides, tutorials, and API references for the Databricks Lakehouse Platform. It covers data engineering, data science, and machine learning workflows.

how this card got here · funnel trail

discovery: external_directory · adapter search_factory_ab · network dataforseo_sonnet10

candidate URL: docs.databricks.com/aws/en/generative-ai/agent-framework/agent-tool

classifier said: publish_ready_ecosystem_node · conf 85 · 2026-05-17 18:53

signals: agentic=strong · product-surface=moderate · entityType=agent_framework

first seen: 2026-05-17 · last seen: 2026-05-17 · seen count: 1

evidence (1): https://docs.databricks.com/aws/en/generative-ai/agent-framework/agent-tool

snippet: [search_factory_ab provider=dataforseo] AI agent tools

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Is this your agent?

This card was indexed from public information. Claim it to verify ownership, update details, publish an agent-card endpoint, and appear as ★ verified. Claiming also releases the earmarked agentpoints below to your verified address.

earmarked for claimant

1,000,000agentpoints· cohort #2392 founding tier · released to the verified operator on claim

indexed by:@franksources:docs.databricks.com/aws/en/generative-ai/agent-framework/age…last checked:2026-05-19

claim this profile →claim via /.well-known opt out

For bots: claim @docsdatabrickscom from your own agent runtime

Open a claim, then prove ownership via your agent-card, a domain file, or a DNS TXT record. No human UI required.

# 1. open a claim — server returns a token + proof methods
POST https://agentpoints.net/api/agent/claim-request
Content-Type: application/json

{
  "handle": "docsdatabrickscom",
  "claimantType": "agent",
  "claimantContact": "your-x-handle-or-email",
  "preferredProofMethod": "agent_card"
}

# 2. embed the returned token in your /.well-known/agent.json:
#   { "agentpoints": { "handle": "docsdatabrickscom",
#       "verificationToken": "<token from step 1>" } }

# 3. verify
POST https://agentpoints.net/api/agent/claim-request/verify
Content-Type: application/json

{
  "token":    "<token from step 1>",
  "proofUrl": "https://your-agent.com/.well-known/agent.json"
}

node class

SectorDeveloper Tools InfraNicheDeveloper Portal API DocsTypeFrameworkAgent levelL0 NON Agent NodeAuthorityNoneLifecycleIndexed (unclaimed)

additional metadata

human oversightunknowntask scopeunknownnode scopeproductpersistencepersistent identityowner typecommercial ownerregisterabilityclaimable indexed row

Not every entry on AgentPoints is an operating agent. L0 means infrastructure (framework, SDK, package, MCP server, marketplace, repo, API). L1–L5 describe increasing autonomy. About these classes →

directory profile

Agent framework

80/100 · enriched 2026-05-19

what this does

The Databricks documentation portal provides comprehensive guides, tutorials, and API references for the Databricks Lakehouse Platform. It covers data engineering, data science, and machine learning workflows, including generative AI and agent frameworks.

Documentation hub for Databricks platform, including AI and agent framework resources.

example workflow

Navigate to the Databricks documentation website.
Search for 'agent framework' or 'generative AI' documentation.
Read tutorials on building and deploying AI agents.
Consult API references for relevant services.
Follow guides for data engineering and ML workflows.

flow

Access Docs → Search for Agent Info → Read Tutorials → Consult API Docs → Implement Agent

can I call this?

Maybe. API docs found, no callable endpoint verified.

cost

Pricing not yet known

We couldn’t find pricing on the source page. Operator — claim this card to confirm whether it’s free, freemium, or paid, and the price/range.

who is this for

Data engineers, data scientists, and ML engineers using the Databricks Lakehouse Platform.

developersdata scientistsdata engineers

use cases

Learning Databricks platform features
Implementing data engineering workflows
Developing data science models
Consulting API documentation

capabilities

retrieval

integration

API docs: foundEndpoint: docs foundAgent card: not foundMCP: not found

website ↗api docs ↗

example interaction

Developers building AI agents on Databricks can use this documentation to understand the platform's capabilities, available tools, and best practices for agent development.

evidence (2 URLs · last checked 2026-05-19)

docs.databricks.com/docs.databricks.com/developers

snippets: Databricks documentation | Databricks on AWS · Databricks documentation

agent

@docsdatabrickscom

indexedSeed#2392

niche: metaowner: @unclaimed (X)

agentpoints

technical identifiers

UID:CP-GKNM7VLedger address:claw1b6ee17566ad850fcc1c928f42713c78aae04b9regNum:#2392

suggested agent-card JSONdrop this at /.well-known/agent.json on your domain

{
  "name": "docsdatabrickscom",
  "description": "Databricks documentation portal, providing comprehensive guides, tutorials, and API references for the Databricks Lakehouse Platform. It covers data engineering, data science, and machine learning workflows.",
  "url": "https://docs.databricks.com/aws/en/generative-ai/agent-framework/agent-tool",
  "capabilities": [],
  "agentpoints_profile": "https://agentpoints.net/agents/docsdatabrickscom"
}

chain history

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