model-exploration

Instructions

When To Use This Skill

Before ANY Honeydew work, set up your session and discover the model:

Step 0: Set workspace and branch Use get_session_workspace_and_branch to check the current session context. If no workspace/branch is set, use list_workspaces, list_workspace_branches, and set_session_workspace_and_branch to select the right workspace and branch. All subsequent tool calls use this context. See the workspace-branch skill for the full tool reference including branch creation, deletion, history, and PRs.

Step 1: List entities Use the list_entities MCP tool to see all entities in the model.

Step 2: Explore entity details Use the get_entity MCP tool with the relevant entity name to list its attributes, metrics, datasets, and relations.

Step 3: Search the model Use the search_model MCP tool to find specific fields, entities, or other objects by name.

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Overview

Honeydew is the Semantic Layer for AI and BI. Honeydew enables a shared source of truth for data teams, providing consistency, flexibility, governance and performance. It provides metrics and attributes over data warehouse tables and views (Snowflake, Databricks, BigQuery) that have relationships defined between them. Use the Honeydew MCP tools to interact with the model.

MCP Tools

Session & Workspace

See the workspace-branch skill for the full reference. Key tools:

• list_workspaces - List all available workspaces (name + warehouse type)
• list_workspace_branches - List branches for a workspace
• get_session_workspace_and_branch - Get current session workspace/branch
• set_session_workspace_and_branch - Set session workspace/branch
• create_workspace_branch - Create a branch (session switches automatically)
• delete_workspace_branch - Delete a branch (destructive — confirm with user first)
• get_branch_history - Get change history for the current branch
• create_pr_for_working_branch - Create a PR for the current working branch

Typical flow:

1. get_session_workspace_and_branch — check if a workspace/branch is already set
2. If not set: list_workspaces → pick a workspace → set_session_workspace_and_branch
3. For development work: create_workspace_branch (session switches to the new branch automatically)

Discovery

• list_entities - List all entities in the model (names, keys, descriptions)
• get_entity - Get detailed info for a specific entity (attributes, metrics, datasets, relations, YAML)
• get_field - Get detailed info for a specific field (attribute or metric) within an entity
• list_domains - List all domains with their names, descriptions, and entities
• get_domain - Get detailed info for a specific domain (entities, filters, parameters, YAML)
• search_model - Search across all model objects (entities, attributes, metrics, datasets, dynamic datasets, domains, parameters). Requires query and search_mode:
  • OR — splits by whitespace, returns objects matching any word
  • AND — splits by whitespace, returns only objects matching all words
  • EXACT — uses the full string as-is, matches name or display name exactly
  • Use entity.field syntax to scope to fields within an entity (e.g. customers.balance finds balance on entities matching customers; customers. returns all fields of matching entities)

Agents & Context

Honeydew has two layers: the semantic layer (entities, metrics, attributes, relations, domains — the data model and business logic such as metric calculations) and the context layer (agents and their associated context items — instructions, skills, knowledge, and memory — that shape how the AI analyst behaves).

• list_agents — List all agents with their names, descriptions, domains, and context references
• get_agent — Get detailed info for a specific agent (domain, context items, welcome message, sample questions)
• list_context_items — List all context items with their types, names, titles, and subtypes
• get_context_item — Get detailed info for a specific context item

Warehouse Discovery

• list_databases - List all databases in the connected data warehouse
• list_schemas - List schemas in a specific database
• list_tables - List tables in the connected data warehouse (requires database and schema parameters)
• get_table_info - Get column-level details for a specific warehouse table

Query Execution

• get_data_from_fields - Execute a query from field parameters and return data (supports limit and offset for pagination)
• get_sql_from_fields - Generate SQL from field parameters without executing

AI-Powered Queries

• ask_deep_analysis_question - Natural language question (simple or complex) → agentic analysis and results. Use conversation_id for follow-up questions.
• abort_deep_analysis_question - Abort an in-progress deep analysis. Requires conversation_id.

Example Usage

Query API Decision Flow

User Request
    │
    ├─► Exact field names known? Want structured query?
    │       └─► YES → get_data_from_fields (deterministic, structured)
    │
    └─► Plain English / natural language / "why" / investigation?
            └─► ask_deep_analysis_question (any complexity)

Tool	Use When	Example Request
get_data_from_fields	Known fields, programmatic	"Get total_revenue by month for 2021"
ask_deep_analysis_question	Plain English questions, trends, root cause	"Show me revenue by city last 2 years"
ask_deep_analysis_question	Complex analysis, "why", multi-step	"Find revenue drops and find contributing factors"

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get_data_from_fields (Primary - Known Fields)

Call get_data_from_fields with field parameters:

• attributes: ["order_header.order_year_month"]
• metrics: ["order_header.total_revenue"]
• filters: ["order_header.order_year_month LIKE '2021%'"]
• order_by: ["\"order_header.order_year_month\" ASC"] — field references must be wrapped in double quotes, like SQL identifiers
• domain: "my_domain" (optional)
• limit: max rows to return (default: 100)
• offset: rows to skip (for pagination)

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get_sql_from_fields (SQL Preview)

Same field parameters as get_data_from_fields, but returns the generated SQL without executing it.

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ask_deep_analysis_question (Natural Language Queries)

Call with:

• question: "Show me revenue by city for the last 2 years" (simple) or "Look at last 5 years, identify revenue drops and find contributing factors" (complex)
• conversation_id: "conv_123" (optional, for follow-up questions)

Returns: markdown analysis report, data, suggested follow-up questions, conversation_id

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Discovery Examples

• Use list_entities to list all entities
• Use get_entity with an entity name to see its attributes, metrics, datasets, and relations
• Use get_field with entity name and field name to get detailed info about a specific field
• Use list_domains to list all domains
• Use get_domain with a domain name to see its entities, filters, parameters, and YAML definition
• Use search_model with a query string and search_mode (OR, AND, or EXACT) to find any model object by name. Use EXACT when you know the precise name; use OR or AND for broad discovery

Documentation Lookup

Use the honeydew-docs MCP tools to search the Honeydew documentation when:

• The user asks conceptual questions ("what is an entity?", "how do metrics work?", "what is a semantic layer?")
• You need to explain Honeydew concepts, architecture, or terminology
• The user is new to Honeydew and needs orientation on capabilities
• You need to understand how a feature works beyond what the MCP tool descriptions provide
• The user asks about advanced modeling concepts or patterns
• The user asks about integrations, setup, or configuration

Search for topics like: "entities", "metrics", "attributes", "domains", "relations", "semantic layer", "governance", or any Honeydew-specific concept.

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Best Practices

1. Use get_entity to explore fields on a specific entity
2. Reference fields using entity.field_name syntax
3. Use discovery tools before any creation tasks
4. For creating entities, metrics, attributes, or relations - use the specialized skills listed above