The Catalog Discovery server allows your AI agents to discover and explore enterprise data assets in Cloud Data Governance and Catalog (CDGC).
Preview Notice:
Effective in the July 2026 release, the Catalog Discovery server is available for private preview. Preview functionality is supported for evaluation purposes but is unwarranted and is not supported in production environments or any environment that you plan to push to production. Informatica intends to include the preview functionality in an upcoming release for production use, but might choose not to in accordance with changing market or technical circumstances. Note that if you are working on a preview POD, all data is excluded from SOC 2 compliance coverage. For more information, contact Informatica Global Customer Support.
Use this MCP server to discover and explore enterprise data assets within Data Governance and Catalog. Your AI agents can search your catalog using natural language or structured keyword queries, retrieve comprehensive asset metadata, and understand relationships between data assets. Use this MCP server to ground your LLM responses, ensuring that your AI only works with governed, accurate enterprise data context.
Key capabilities
This MCP server offers the following key capabilities:
•Natural language search. Discovers data assets using conversational queries. The AI interprets intent, relationships, and implicit filters from plain English.
•Asset details. Retrieves full 360-degree metadata for any asset including stakeholders resolved to names or emails, classifications, glossary terms, hierarchy, and neighbourhood relationships.
•Keyword search. Provides precise, structured queries with typed filters (classType, resourceType, certified, lifecycle, stakeholders, dates), aggregations for faceted exploration, and multi-field sorting.
•Identity resolution. Resolves stakeholder identities to human-readable names and emails for AI consumption.
Use cases
Use this MCP server to address the following use cases:
•Data discovery. Find relevant data assets across your catalog using natural language or structured queries.
•Data governance reporting. Quickly identify and report on assets by classification, certification status, or stakeholder ownership.
•LLM response grounding. Provide your AI with governed enterprise data context to prevent hallucination. Examples of context are names, types, descriptions, and lineage.
•Relationship exploration. Understand how assets connect through hierarchy, neighbourhood, and glossary linkages.
Tools
The following table describes the tools available on this MCP server:
Tool
Description
get_asset_details
Fetches full details of an asset by identity.
Use search_assets first to discover asset identities.
Returns a resolvedSummary plus raw systemAttributes. The resolvedSummary includes the following attributes:
- identity
- name
- type
- location
- description
- stakeholders resolved to names or email addresses
- asset groups
- timestamps
Use segments to request the following additional aspects:
- selfAttributes
- stakeholdership
- dataClassification
- glossary
- hierarchy
- neighbourhood
search_assets
Searches for assets in your catalog using keyword search or natural language (NL) search.
KEYWORD mode is the default. Use it first.
Use KEYWORD mode with filterSpec for precise structured queries.
By default, KEYWORD searches for "contains" matches in common fields. Examples of common fields are name, description, and alias name.
KEYWORD mode searches across the following fields:
- core.name
- com.infa.ccgf.models.governance.AliasNames
- core.businessName
- core.description
- core.technicalDescription
- core.externalIdentity
You can also provide filters within a natural language (NL) query . For example, you can include the filter "show Oracle tables modified after 2025-01-20." If KEYWORD mode does not return expected results, retry with NL mode.
The following are NL query examples:
- "domain named finance"
- "Assets with classifications"
- "Assets with Glossaries"
- "Show me classifications"
- "Show me Glossaries"
- "Oracle Tables"
- "find the GDPR policy"
- "show BI reports related to policy GDPR"
- "show me business terms related to tables"
- "show columns related to business term Phone Number"
- "show datasets related to business term Customer"
- "show columns from table customers"
- "show data elements from dataset finance"
- "find data elements in the EMPLOYEE dataset"
- "find folders with customer files"
- "show everything in domain HIPAA"
- "show domains linked to policy GDPR"
- "show datasets not linked to business term"
- "show data elements not connected to data classification"
- "my business terms"
- "show domains certified by me"
- "show tables modified by me"
- "show business terms that contain columns and are related to data elements"
- "show all items named cluster or updated by system"
- "show business terms where lifecycle is In Review or Draft"
- "show assets modified after 2025-01-20"
- "find Payroll subdomains with Salary business terms"
Use * as a query to search your entire catalog and filter or aggregate results.
This query returns resolved asset summaries with the following attributes:
- identity
- class_type
- name
- description
- stakeholders
- asset_groups
The response includes aggregations for narrowing results and suggested_next_steps for iterative refinement. Use get_asset_details to dig deeper into a specific asset returned by this search.
CDGC organizes assets into the following categories:
- Glossary. Business vocabulary and definitions. The glossary includes Domain, SubDomain, BusinessTerm, and Metric.
- Data catalog: Technical assets harvested from your catalog sources. Technical assets are tables, views, columns, schemas, and files.
A regular hierarchy includes core.Resource > core.DataSource(Database/Schema) > core.Dataset(Table) > core.DataElement(Column).
Filter by core.ResourceType to filter assets of a particular kind. Examples of resource types are Oracle and Amazon S3.
The following are high-level resource types:
- System and data set. Business-friendly representations of technical infrastructure. Examples are System, DataSet, and DataElement.
- Process. Business activities and workflows.
- Policy. Internal rules and data governance guidance.
- Project. Planned activities to achieve governance objectives.
- AI. ML models and systems. Examples are AIModel and AISystem.
- Business Area, Legal Entity, Geography, Regulation
Note:
In AI Agent Engineering, the MCP server tools are called "actions."