Collate MCP Tools Reference

Overview

This document provides detailed examples and usage patterns for all available Collate MCP tools. Each tool includes sample requests, responses, and common use cases.

Available Tools

Category	Tools
Discover	search_metadata, semantic_search, search_company_context, nlq_search
Inspect	get_entity_details, get_company_context
Lineage & Impact	get_entity_lineage, create_lineage, root_cause_analysis
Knowledge	create_glossary, create_glossary_term, create_context_memory, create_article
Govern & Classify	create_classification, create_tag, create_domain, create_data_product, patch_entity
Data Quality	get_test_definitions, create_test_case
Metrics	create_metric

Which Tool to Call First

If you already have a fully qualified name (from a prior result, a user-supplied FQN, or a UI deep link), start with get_entity_details directly. If you don’t, start with search_metadata or semantic_search first, then pass the fullyQualifiedName from a result into get_entity_details. Starting broad with search_metadata when you already know the FQN adds unnecessary round trips (re-searching, re-confirming the match) that calling the lookup tool directly avoids.

Fully Qualified Name (FQN) Format

An entity’s FQN is a dot-separated path built from the entity and its ancestors. The number of segments depends on entity type:

Entity Type	FQN Pattern	Example
Table	`service.database.schema.table`	`snowflake_prod.analytics.public.customers`
Database Schema	`service.database.schema`	`snowflake_prod.analytics.public`
Database	`service.database`	`snowflake_prod.analytics`
Dashboard	`service.dashboardName`	`sample_superset.10`
Tag	`Classification.TagName`	`PII.Sensitive`
Glossary Term	`glossary.term`	`marketing-terms.customer-acquisition-cost`

Special Characters: If a name segment contains a period (.) or a double quote ("), wrap that segment in double quotes, escaping any internal " by doubling it. For example, a schema literally named sales.eu inside database prod becomes service.prod."sales.eu".table.

Discover

Search and find data assets across your catalog using keyword, semantic, or natural language queries.

search_metadata

Description: Find data assets and business terms in your Collate catalog. Use Cases:

Discover tables containing specific data
Find dashboards related to business areas
Search for glossary terms
Locate pipelines by name or description

Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Keywords to search for
`entityType`	string	No	Filter by entity type (table, topic, dashboard, etc.)
`size`	integer	No	Max results to return (default: 10)
`fields`	string	No	Comma-separated additional fields to include
`queryFilter`	string	No	Advanced: direct OpenSearch DSL JSON query. Example: `{"bool": {"must": [{"term": {"entityType": "table"}}, {"term": {"tags.tagFQN": "PII.Sensitive"}}]}}`

Omit queryFilter entirely rather than sending an empty string, "null", or "{}". A degenerate value is not treated as “no filter”: it currently produces a 400 JSON parsing failed error instead of falling through to a normal keyword search.

Entity Types

Service Entities: databaseService, messagingService, apiService, dashboardService, pipelineService, storageService, mlmodelService, metadataService, searchService
Data Asset Entities: apiCollection, apiEndpoint, table, storedProcedure, database, databaseSchema, dashboard, dashboardDataModel, pipeline, chart, topic, searchIndex, mlmodel, container
User Entities: user, team
Domain Entities: domain, dataProduct
Governance Entities: metric, glossary, glossaryTerm

Examples Basic Search:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "search_metadata",
    "arguments": {
      "query": "sales"
    }
  }
}

Search for Specific Entity Type:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "search_metadata",
    "arguments": {
      "query": "customer",
      "entityType": "table",
      "size": 15
    }
  }
}

Search with Additional Fields:

{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "tools/call",
  "params": {
    "name": "search_metadata",
    "arguments": {
      "query": "order",
      "entityType": "table",
      "fields": "columns,queries,upstreamLineage",
      "size": 5
    }
  }
}

Sample Response:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Found 5 results matching 'sales':\n\n**Tables:**\n1. **sales_transactions** (mysql_prod.ecommerce.public.sales_transactions)\n   - Description: Daily sales transaction records\n   - Service: mysql_prod (MySQL)\n   - Owners: sales-team, john.doe@company.com\n   - Tags: PII, Financial\n   - [View in OpenMetadata](https://your-om.com/table/mysql_prod.ecommerce.public.sales_transactions)\n\n2. **sales_reports** (postgresql_analytics.reporting.main.sales_reports)\n   - Description: Aggregated sales reporting data\n   - Service: postgresql_analytics (PostgreSQL)\n   - Owners: analytics-team\n   - [View in OpenMetadata](https://your-om.com/table/postgresql_analytics.reporting.main.sales_reports)\n\n**Dashboards:**\n3. **Sales Performance Dashboard** (looker_prod.sales.sales_performance)\n   - Description: Real-time sales KPI dashboard\n   - Service: looker_prod (Looker)\n   - Charts: 8 charts\n   - [View in OpenMetadata](https://your-om.com/dashboard/looker_prod.sales.sales_performance)"
      }
    ]
  }
}

semantic_search

Description: Meaning-based discovery of data assets using vector embeddings. Use this for exploratory or vague queries where exact names are unknown — it finds conceptually related assets even when keywords don’t match. Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Natural language description of the data you’re looking for
`filters`	object	No	Optional filters. Supported keys: `entityType` (array), `service` (array), `tags` (array)
`size`	integer	No	Number of results to return. Default is 10, maximum is 50
`k`	integer	No	Number of nearest neighbors to consider internally (KNN parameter). Default is 100
`threshold`	number	No	Minimum similarity score (0.0–1.0). Default is 0.0 (no filtering). Use `0.5`–`0.7` for high-confidence results

Examples Conceptual asset discovery:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "semantic_search",
    "arguments": {
      "query": "customer purchase history and spending behavior"
    }
  }
}

Filtered semantic search:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "semantic_search",
    "arguments": {
      "query": "revenue forecasting metrics",
      "filters": { "entityType": ["dashboard"], "service": ["BigQuery"] },
      "threshold": 0.5
    }
  }
}

search_company_context

Description: Semantic search over company context knowledge pills from the Context Center. Returns matching pills with their title, question, answer, summary, and source file. Use this to answer questions from company documents, runbooks, FAQs, and policies. Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Natural-language question or topic to find relevant company knowledge for
`size`	integer	No	Number of pills to return. Default is 10, maximum is 50

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "search_company_context",
    "arguments": {
      "query": "data retention policy",
      "size": 5
    }
  }
}

nlq_search

Description: Natural language query search across data assets. Interprets free-form questions and returns matching assets from the catalog. The index parameter accepts entity type names (e.g., table, dashboard) or the dataAsset alias for a broad cross-entity search. Parameters

Parameter	Type	Required	Description
`query`	string	No	Natural language query. Defaults to `*` (return all)
`limit`	integer	No	Maximum number of results to return. Default is 10
`include_deleted`	boolean	No	Include deleted entities in results. Default is `false`
`index`	string	No	Entity type or index to search. Default is `dataAsset`. Accepts entity types (e.g., `table`, `testCase`) or raw index names

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "nlq_search",
    "arguments": {
      "query": "show me all Tier 1 tables in the Finance domain",
      "limit": 10
    }
  }
}

Inspect

Retrieve detailed information about specific entities and company knowledge pills.

get_entity_details

Description: Retrieve detailed information about a specific entity using its fully qualified name. Parameters

Parameter	Type	Required	Description
`entityType`	string	Yes	Type of entity (table, topic, dashboard, etc.)
`fqn`	string	Yes	Fully qualified name of the entity

Examples Get Table Details:

{
  "jsonrpc": "2.0",
  "id": 4,
  "method": "tools/call",
  "params": {
    "name": "get_entity_details",
    "arguments": {
      "entityType": "table",
      "fqn": "mysql_prod.ecommerce.public.customer_orders"
    }
  }
}

Get Dashboard Details:

{
  "jsonrpc": "2.0",
  "id": 5,
  "method": "tools/call",
  "params": {
    "name": "get_entity_details",
    "arguments": {
      "entityType": "dashboard",
      "fqn": "superset_prod.sales.quarterly_revenue"
    }
  }
}

Sample Response:

{
  "jsonrpc": "2.0",
  "id": 4,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "## Table: customer_orders\n\n**Fully Qualified Name**: mysql_prod.ecommerce.public.customer_orders\n\n**Basic Information**:\n- **Service**: mysql_prod (MySQL)\n- **Database**: ecommerce\n- **Schema**: public\n- **Type**: Regular Table\n- **Description**: Stores all customer order transactions with order details, timestamps, and status information\n\n**Ownership & Governance**:\n- **Owners**: ecommerce-team, sarah.johnson@company.com\n- **Tags**: PII, Financial, Customer-Data\n- **Tier**: Tier1\n\n**Schema** (5 columns):\n1. **order_id** (BIGINT) - Primary key, unique order identifier\n2. **customer_id** (BIGINT) - Foreign key to customer table\n3. **order_date** (TIMESTAMP) - When the order was placed\n4. **total_amount** (DECIMAL) - Total order value\n5. **status** (VARCHAR) - Order status (pending, completed, cancelled)\n\n**Usage Statistics**:\n- **Row Count**: ~2.5M rows\n- **Size**: 450 MB\n- **Last Updated**: 2024-01-15 09:30:00\n\n[View in OpenMetadata](https://your-om.com/table/mysql_prod.ecommerce.public.customer_orders)"
      }
    ]
  }
}

get_company_context

Description: Fetch a single company context knowledge pill by its fully qualified name. Returns the full title, question, answer, summary, and source file. Use the fullyQualifiedName or name returned by search_company_context. Parameters

Parameter	Type	Required	Description
`fqn`	string	Yes	The `fullyQualifiedName` or `name` of the knowledge pill from a `search_company_context` result. Pass it verbatim

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_company_context",
    "arguments": {
      "fqn": "data-retention-policy.gdpr-rules"
    }
  }
}

Lineage & Impact

Explore data dependencies, trace upstream sources, and analyze downstream impact.

get_entity_lineage

Description: Retrieve upstream and downstream lineage information for any entity to understand data dependencies and impact analysis. Parameters

Parameter	Type	Required	Description
`entityType`	string	Yes	Type of entity
`fqn`	string	Yes	Fully qualified name of the entity
`upstreamDepth`	integer	No	Number of upstream hops to traverse (default: 3)
`downstreamDepth`	integer	No	Number of downstream hops to traverse (default: 3)
`includeColumnLineage`	boolean	No	Include column-level lineage mappings. Default is `false` (table-level only)

create_lineage

Description: Create a lineage relationship between two data assets. Requires the entity IDs (UUIDs), which you can retrieve using get_entity_details or search_metadata. Parameters

Parameter	Type	Required	Description
`fromEntity`	object	Yes	Source entity with `type` (entity type string) and `id` (UUID)
`toEntity`	object	Yes	Destination entity with `type` and `id`

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_lineage",
    "arguments": {
      "fromEntity": {
        "type": "table",
        "id": "a1b2c3d4-0000-0000-0000-111111111111"
      },
      "toEntity": {
        "type": "table",
        "id": "e5f6a7b8-0000-0000-0000-222222222222"
      }
    }
  }
}

root_cause_analysis

Description: Perform root cause analysis via data quality lineage. Identifies upstream failures causing issues on the specified asset, then analyzes downstream impact. If status is failed, check upstreamAnalysis for root causes and downstreamAnalysis for impact. Parameters

Parameter	Type	Required	Description
`fqn`	string	Yes	Fully qualified name of the entity to analyze
`entityType`	string	Yes	Type of the entity (e.g., `table`, `dashboard`)
`upstreamDepth`	integer	No	Number of levels upstream to traverse. Default is 3
`downstreamDepth`	integer	No	Number of levels downstream to traverse (only used when upstream failures are found). Default is 3
`includeColumnLineage`	boolean	No	Include column-level lineage on edges. Default is `false` (table-level only)
`includeDeleted`	boolean	No	Include deleted entities. Default is `false`

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "root_cause_analysis",
    "arguments": {
      "fqn": "bigquery_prod.analytics.reporting.sales_summary",
      "entityType": "table",
      "upstreamDepth": 3,
      "downstreamDepth": 3
    }
  }
}

Knowledge

Create and manage glossaries, terms, articles, and reusable context memories.

create_glossary

Description: Create a new glossary to organize business terms and definitions. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the glossary
`description`	string	Yes	Description of the glossary
`owners`	array	No	List of owners (users or teams)
`reviewers`	array	No	List of reviewers (users or teams)
`mutuallyExclusive`	boolean	Yes	Whether terms are mutually exclusive

Examples Create Business Glossary:

{
  "jsonrpc": "2.0",
  "id": 6,
  "method": "tools/call",
  "params": {
    "name": "create_glossary",
    "arguments": {
      "name": "Marketing Terms",
      "description": "Collection of marketing-related terms and definitions used across campaigns, analytics, and reporting",
      "owners": ["marketing-team", "alice.smith@company.com"],
      "reviewers": ["data-governance-team"],
      "mutuallyExclusive": false
    }
  }
}

Create Technical Glossary:

{
  "jsonrpc": "2.0",
  "id": 7,
  "method": "tools/call",
  "params": {
    "name": "create_glossary",
    "arguments": {
      "name": "Data Quality Metrics",
      "description": "Standardized definitions for data quality measurements and KPIs",
      "owners": ["data-quality-team"],
      "mutuallyExclusive": true
    }
  }
}

Sample Response:

{
  "jsonrpc": "2.0",
  "id": 6,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "✅ Successfully created glossary: **Marketing Terms**\n\n**Details**:\n- **Name**: Marketing Terms\n- **Description**: Collection of marketing-related terms and definitions used across campaigns, analytics, and reporting\n- **Owners**: marketing-team, alice.smith@company.com\n- **Reviewers**: data-governance-team\n- **Mutually Exclusive**: No\n- **Status**: Active\n\n**Next Steps**:\n- You can now add glossary terms to this glossary\n- Consider creating hierarchical terms for better organization\n- Set up approval workflows if needed\n\n[View Glossary in OpenMetadata](https://your-om.com/glossary/marketing-terms)"
      }
    ]
  }
}

create_glossary_term

Description: Create a new term within an existing glossary, with support for hierarchical relationships. Parameters

Parameter	Type	Required	Description
`glossary`	string	Yes	FQN of the parent glossary
`name`	string	Yes	Name of the term
`description`	string	Yes	Definition of the term
`parentTerm`	string	No	FQN of parent term for hierarchy
`owners`	array	No	List of owners (users or teams)

Examples Create Root Level Term:

{
  "jsonrpc": "2.0",
  "id": 8,
  "method": "tools/call",
  "params": {
    "name": "create_glossary_term",
    "arguments": {
      "glossary": "marketing-terms",
      "name": "Customer Acquisition Cost",
      "description": "The total cost of acquiring a new customer, including marketing and sales expenses divided by the number of customers acquired in a specific period",
      "owners": ["marketing-team", "finance-team"]
    }
  }
}

Create Child Term:

{
  "jsonrpc": "2.0",
  "id": 9,
  "method": "tools/call",
  "params": {
    "name": "create_glossary_term",
    "arguments": {
      "glossary": "marketing-terms",
      "parentTerm": "marketing-terms.customer-acquisition-cost",
      "name": "Organic CAC",
      "description": "Customer acquisition cost specifically for customers acquired through organic channels (SEO, word of mouth, etc.) without paid advertising",
      "owners": ["marketing-team"]
    }
  }
}

Sample Response:

{
  "jsonrpc": "2.0",
  "id": 8,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "✅ Successfully created glossary term: **Customer Acquisition Cost**\n\n**Details**:\n- **Glossary**: Marketing Terms\n- **Full Path**: marketing-terms.customer-acquisition-cost\n- **Definition**: The total cost of acquiring a new customer, including marketing and sales expenses divided by the number of customers acquired in a specific period\n- **Owners**: marketing-team, finance-team\n- **Status**: Draft\n\n**Suggestions**:\n- Consider adding related terms like 'Customer Lifetime Value' and 'Return on Ad Spend'\n- You might want to create child terms for different acquisition channels\n- Add synonyms if this term is known by other names in your organization\n\n[View Term in OpenMetadata](https://your-om.com/glossary/marketing-terms/customer-acquisition-cost)"
      }
    ]
  }
}

create_context_memory

Description: Save a reusable piece of knowledge to the Context Center — a preference, instruction, runbook step, or FAQ answer the assistant should retain across conversations. Use this when the user explicitly asks you to remember, note, or store something. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Stable, unique system name. Reusing an existing name updates that memory
`question`	string	Yes	The canonical question or instruction this memory represents
`answer`	string	Yes	The canonical answer or guidance to retain
`title`	string	No	Short human-readable title shown in the Context Center
`summary`	string	No	One-line summary of the memory
`memoryType`	string	No	`Preference`, `UseCase`, `Note`, `Runbook`, or `Faq`. Defaults to `Note`
`memoryScope`	string	No	`UserGlobal` (broad) or `EntityScoped` (tied to a specific entity)
`owners`	array	No	Collate usernames or team names
`tags`	array	No	Tag FQNs to apply
`domains`	array	No	Domain FQNs this memory belongs to

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_context_memory",
    "arguments": {
      "name": "finance-warehouse-preference",
      "title": "Finance Dashboard Warehouse",
      "question": "Which warehouse should finance dashboards query?",
      "answer": "Always query the FINANCE_PROD warehouse for finance dashboards.",
      "memoryType": "Preference",
      "memoryScope": "UserGlobal",
      "domains": ["Finance"]
    }
  }
}

create_article

Description: Create a new knowledge article in the Collate knowledge base. Articles can be nested under a parent page and support an approval workflow when reviewers are set. Valid entityStatus values are: Draft, In Review, Approved, Deprecated, Rejected, Unprocessed. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Unique system name for the article
`displayName`	string	No	Human-readable display name
`description`	string	No	Article content in Markdown format
`owners`	array	No	Collate usernames or team names to set as owners
`reviewers`	array	No	Collate usernames or team names to set as reviewers. When set, the article enters an approval workflow
`tags`	array	No	Tag FQNs to apply (e.g., `Tier.Tier1`)
`domains`	array	No	Domain FQNs this article belongs to
`entityStatus`	string	No	Lifecycle status: `Draft`, `In Review`, `Approved`, `Deprecated`, `Rejected`, or `Unprocessed`
`parent`	string	No	FQN of the parent knowledge page to nest this article under

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_article",
    "arguments": {
      "name": "orders-table-guide",
      "displayName": "Orders Table Guide",
      "description": "## Overview\nThe `orders` table records all customer purchase transactions...",
      "owners": ["data-platform-team"],
      "reviewers": ["data-governance-team"],
      "tags": ["Tier.Tier1"],
      "domains": ["Finance"],
      "entityStatus": "Draft"
    }
  }
}

Govern & Classify

Define and apply classifications, tags, domains, data products, and entity updates.

create_classification

Description: Create a new Classification in Collate. A Classification is a top-level container that groups related Tags (e.g., PII, Tier). The name becomes the root segment of every tag FQN under it. The mutuallyExclusive flag is immutable once the classification exists. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the classification. Becomes the root segment of tag FQNs (e.g., `PII` → `PII.Sensitive`). Must be unique.
`description`	string	Yes	Description of the classification in Markdown format.
`displayName`	string	No	Human-readable display name.
`mutuallyExclusive`	boolean	No	If `true`, an entity can only carry one tag from this classification at a time. Immutable after creation.
`owners`	array	No	Collate usernames or team names to set as owners. Each must already exist.
`reviewers`	array	No	Collate usernames or team names to set as reviewers. Each must already exist.
`domains`	array	No	FQNs of domains this classification belongs to. Each domain must already exist.

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_classification",
    "arguments": {
      "name": "PII",
      "description": "Tags for classifying Personally Identifiable Information across data assets.",
      "mutuallyExclusive": false,
      "owners": ["data-governance-team"],
      "reviewers": ["privacy-team"]
    }
  }
}

create_tag

Description: Create a new Tag inside an existing Classification in Collate. The tag FQN is Classification.TagName (e.g., PII.Sensitive). At least one of classification or parent must be provided. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the tag (the leaf segment, e.g., `Sensitive`). Must be unique within its parent.
`description`	string	Yes	Description of the tag in Markdown format.
`classification`	string	No	Name of the classification this tag belongs to (e.g., `PII`). Must already exist. Optional if `parent` is provided.
`parent`	string	No	FQN of the parent tag for a nested tag (e.g., `PII.PersonalData`). Must already exist. Omit for a top-level tag.
`displayName`	string	No	Human-readable display name.
`mutuallyExclusive`	boolean	No	If `true`, child tags under this tag are mutually exclusive on an entity.
`owners`	array	No	Collate usernames or team names to set as owners. Each must already exist.
`reviewers`	array	No	Collate usernames or team names to set as reviewers. Each must already exist.
`domains`	array	No	FQNs of domains this tag belongs to. Each domain must already exist.

Examples Create a top-level tag:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_tag",
    "arguments": {
      "name": "Sensitive",
      "description": "Data that contains sensitive personal information requiring strict access controls.",
      "classification": "PII"
    }
  }
}

Create a nested tag:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "create_tag",
    "arguments": {
      "name": "Email",
      "description": "Email addresses — a subset of sensitive personal data.",
      "parent": "PII.Sensitive"
    }
  }
}

create_domain

Description: Create a new Domain in Collate. A Domain is a top-level governance grouping of data assets. To create a child domain, set parent to the FQN of an existing parent domain. domainType defaults to Aggregate if omitted. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the domain. Must be unique.
`description`	string	Yes	Description of the domain in Markdown format.
`domainType`	string	No	Type of domain: `Source-aligned`, `Consumer-aligned`, or `Aggregate` (default).
`parent`	string	No	FQN of an existing parent domain when creating a child domain. Omit for a top-level domain.
`displayName`	string	No	Human-readable display name.
`experts`	array	No	Collate login names of domain experts (e.g., `john.doe`). Each must already exist.
`owners`	array	No	Collate usernames or team names to set as owners. Each must already exist.
`tags`	array	No	Tag FQNs to apply to this domain (e.g., `Tier.Tier1`).

Examples Create a top-level domain:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_domain",
    "arguments": {
      "name": "Finance",
      "description": "Domain for all finance and accounting data assets.",
      "domainType": "Consumer-aligned",
      "experts": ["jane.doe"],
      "owners": ["finance-team"]
    }
  }
}

Create a child domain:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "create_domain",
    "arguments": {
      "name": "Revenue",
      "description": "Sub-domain covering revenue tracking and forecasting.",
      "domainType": "Aggregate",
      "parent": "Finance"
    }
  }
}

create_data_product

Description: Create a new Data Product in Collate. A Data Product groups data assets that deliver business value and must belong to at least one Domain. All referenced domain FQNs must already exist. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the data product. Must be unique.
`description`	string	Yes	Description of the data product in Markdown format.
`domains`	array	Yes	FQNs of the domains this data product belongs to (e.g., `Finance`). At least one required; each must already exist.
`displayName`	string	No	Human-readable display name.
`experts`	array	No	Collate login names of data product experts (e.g., `john.doe`). Each must already exist.
`owners`	array	No	Collate usernames or team names to set as owners. Each must already exist.
`reviewers`	array	No	Collate usernames or team names to set as reviewers. Each must already exist.
`tags`	array	No	Tag FQNs to apply to this data product (e.g., `Tier.Tier1`, `PII.Sensitive`).

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_data_product",
    "arguments": {
      "name": "customer_360",
      "displayName": "Customer 360",
      "description": "Unified view of customer data including orders, support history, and lifetime value.",
      "domains": ["Finance", "Marketing"],
      "experts": ["alice.smith"],
      "owners": ["data-platform-team"],
      "tags": ["Tier.Tier1"]
    }
  }
}

patch_entity

Description: Update an entity using JSON Patch operations. Always look up the entity first to get its current state and UUID before patching. Array fields use plural names in paths: /domains, /owners, /tags, /reviewers. Parameters

Parameter	Type	Required	Description
`entityType`	string	Yes	Type of entity to patch (e.g., `table`, `glossaryTerm`)
`fqn`	string	Yes	Fully qualified name of the entity to patch
`patch`	string	Yes	JSON Patch operations array as a JSON string. Each operation has `op` (`add`/`remove`/`replace`), `path`, and `value`

Examples Update a description:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "patch_entity",
    "arguments": {
      "entityType": "table",
      "fqn": "mysql_prod.ecommerce.public.orders",
      "patch": "[{\"op\": \"add\", \"path\": \"/description\", \"value\": \"Stores all customer orders with status and totals.\"}]"
    }
  }
}

Add a domain:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "patch_entity",
    "arguments": {
      "entityType": "table",
      "fqn": "mysql_prod.ecommerce.public.orders",
      "patch": "[{\"op\": \"add\", \"path\": \"/domains/0\", \"value\": {\"id\": \"<domain-uuid>\", \"type\": \"domain\", \"name\": \"Finance\"}}]"
    }
  }
}

Data Quality

Access test definitions and create test cases to validate your data assets.

get_test_definitions

Description: List available test definitions that can be used to create data quality test cases. Use entityType=TABLE for table-level tests and entityType=COLUMN for column-level tests. Pass results to create_test_case. Parameters

Parameter	Type	Required	Description
`entityType`	string	Yes	`TABLE` for table-level tests or `COLUMN` for column-level tests
`testPlatform`	string	No	Test platform filter. Default is `OpenMetadata`. Other options: `GreatExpectations`, `DBT`, `Deequ`, `Soda`, `Other`
`limit`	integer	No	Maximum number of results to return. Default is 10
`after`	string	No	Cursor value from a previous response’s `nextCursor` field for pagination

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_test_definitions",
    "arguments": {
      "entityType": "TABLE",
      "testPlatform": "OpenMetadata",
      "limit": 10
    }
  }
}

create_test_case

Description: Create a data quality test case for a table or column. Use get_test_definitions first to find the right test definition and its required parameters. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the test case
`fqn`	string	Yes	Fully qualified name of the table to test. Always use the table FQN, not a column FQN
`testDefinitionName`	string	Yes	Name of the test definition to use
`parameterValues`	array	Yes	Array of `{ "name": "<param>", "value": "<value>" }` objects matching the test definition’s parameters
`columnName`	string	No	Column name (leaf only, e.g., `shop_id`) for column-level tests
`description`	string	No	Description of the test case

Examples Table-level row count test:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_test_case",
    "arguments": {
      "name": "orders_row_count_check",
      "fqn": "mysql_prod.ecommerce.public.orders",
      "testDefinitionName": "tableRowCountToBeBetween",
      "parameterValues": [
        { "name": "minValue", "value": "1000" },
        { "name": "maxValue", "value": "10000000" }
      ],
      "description": "Ensures the orders table always has rows within expected bounds."
    }
  }
}

Column uniqueness test:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "create_test_case",
    "arguments": {
      "name": "orders_id_uniqueness",
      "fqn": "mysql_prod.ecommerce.public.orders",
      "testDefinitionName": "columnValuesToBeUnique",
      "columnName": "order_id",
      "parameterValues": []
    }
  }
}

Metrics

Define and track measurable business and technical KPIs.

create_metric

Description: Create a new Metric entity representing a measurable business or technical KPI (e.g., daily revenue, error rate). Metrics can be expressed in SQL, Python, Java, or JavaScript. Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Unique name for the metric. Must not contain `::`
`metricExpressionLanguage`	string	Yes	Language for the expression: `SQL`, `Java`, `JavaScript`, `Python`, or `External`
`metricExpressionCode`	string	Yes	Code or query that computes the metric (e.g., a SQL `SELECT` statement)
`description`	string	No	Description in Markdown format
`displayName`	string	No	Human-readable display name
`metricType`	string	No	`COUNT`, `SUM`, `AVERAGE`, `RATIO`, `PERCENTAGE`, `MIN`, `MAX`, `MEDIAN`, `MODE`, `STANDARD_DEVIATION`, `VARIANCE`, or `OTHER`
`granularity`	string	No	Time granularity: `SECOND`, `MINUTE`, `HOUR`, `DAY`, `WEEK`, `MONTH`, `QUARTER`, or `YEAR`
`unitOfMeasurement`	string	No	`COUNT`, `DOLLARS`, `PERCENTAGE`, `TIMESTAMP`, `SIZE`, `REQUESTS`, `EVENTS`, `TRANSACTIONS`, or `OTHER`
`owners`	array	No	Collate usernames or team names to set as owners
`tags`	array	No	Tag FQNs (e.g., `Tier.Tier1`)
`domains`	array	No	Domain FQNs this metric belongs to

Example

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "create_metric",
    "arguments": {
      "name": "daily_revenue",
      "displayName": "Daily Revenue",
      "description": "Total revenue from completed orders for a given day.",
      "metricExpressionLanguage": "SQL",
      "metricExpressionCode": "SELECT SUM(total_amount) FROM orders WHERE status = 'completed' AND DATE(order_date) = CURRENT_DATE",
      "metricType": "SUM",
      "granularity": "DAY",
      "unitOfMeasurement": "DOLLARS",
      "owners": ["finance-team"],
      "tags": ["Tier.Tier1"]
    }
  }
}

​Collate MCP Tools Reference

​Overview

​Available Tools

​Which Tool to Call First

​Fully Qualified Name (FQN) Format

​Discover

​search_metadata

​semantic_search

​search_company_context

​nlq_search

​Inspect

​get_entity_details

​get_company_context

​Lineage & Impact

​get_entity_lineage

​create_lineage

​root_cause_analysis

​Knowledge

​create_glossary

​create_glossary_term

​create_context_memory

​create_article

​Govern & Classify

​create_classification

​create_tag

​create_domain

​create_data_product

​patch_entity

​Data Quality

​get_test_definitions

​create_test_case

​Metrics

​create_metric

Collate MCP Tools Reference

Overview

Available Tools

Which Tool to Call First

Fully Qualified Name (FQN) Format

Discover

search_metadata

semantic_search

search_company_context

nlq_search

Inspect

get_entity_details

get_company_context

Lineage & Impact

get_entity_lineage

create_lineage

root_cause_analysis

Knowledge

create_glossary

create_glossary_term

create_context_memory

create_article

Govern & Classify

create_classification

create_tag

create_domain

create_data_product

patch_entity

Data Quality

get_test_definitions

create_test_case

Metrics

create_metric