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AgentQ in Action

Qualytics implements the Model Context Protocol (MCP) as a server that provides authorized access to shared data quality context and actions through tools. This page covers the endpoint, authentication, available capabilities, and example conversations so you can understand how AgentQ and external MCP clients interact with the Qualytics platform.

Info

AgentQ enforces limits on rate, tokens, timeouts, and query types to ensure fair usage and platform stability. See AgentQ Limits for full details.

Endpoint

The MCP service is available at your Qualytics instance URL:

https://your-qualytics-instance.qualytics.io/mcp

Authentication

The MCP service uses the same authentication mechanism as the Qualytics API. You'll need a Personal API Token (PAT) to authenticate requests. Include it in the Authorization header:

Authorization: Bearer YOUR_API_TOKEN

To generate a token, navigate to Settings > Tokens and click Generate Token. For detailed instructions, see Tokens.

Capabilities

This video shows one example completed from a single natural-language prompt. In the recorded scenario, AgentQ joins data from Databricks and BigQuery, aggregates monthly customer spending, and creates a quality check. AgentQ infers details such as join keys, rule types, and field mappings from the available context. Results depend on the data, permissions, prompt, provider, and model. Review the inferred details and created assets before using them.

Datastore Exploration

When you connect an AI assistant to Qualytics via MCP, it gains the ability to explore your data landscape and understand the structure of your datastores:

  • "What tables are in our sales database?"
  • "Show me the schema for the customer_orders table"
  • "What fields are available in the transactions container?"

Data Transformations

Create computed assets through conversation instead of manually configuring them in the UI:

  • Computed Tables: SQL queries stored and executed in the source database using its native dialect.
  • Computed Files: SQL transformations over file-based sources like S3, ADLS, or GCS.
  • Cross-Datastore Joins: Join data across supported systems, such as Snowflake and PostgreSQL or BigQuery and S3.
  • Computed Fields: Add derived or type-cast fields to existing containers using custom expressions.

Quality Check Management

Create and manage data quality checks through natural conversation:

  • "Make sure the email field in the customers table is never null"
  • "Add a check that order_total is always between 0 and 1,000,000"
  • "Verify that ship_date is always after order_date"

AgentQ can select a rule type and parameters from your request and the context available to it. Before relying on the check, confirm that its fields, conditions, filters, and other settings match the business expectation.

Inside a quality check or check template dialog, the embedded AgentQ assistant can turn a plain-language requirement into a proposal card. Select Apply to form to preview the suggested settings in the form, review the highlighted changes, and make any corrections. The check or template is not saved until you select Save.

Drafting Controls from Regulatory Documents

AgentQ can help extract candidate requirements from regulatory publications such as BCBS 239 (Principles for effective risk data aggregation and risk reporting) and draft tagged quality checks for review. A qualified subject matter expert must determine whether a requirement applies, confirm that each check represents it correctly, and approve the control before use. Tags and descriptions can help preserve a reference to the source requirement, but they do not establish compliance on their own.

Anomaly Investigation

Investigate quality issues conversationally:

  • "Tell me about the anomalies found in yesterday's scan"
  • "What's wrong with anomaly 12345?"
  • "Explain the business impact of the data quality issues in the orders table"

Operations, Notifications, and Ticketing

Beyond analysis, AgentQ can take actions allowed by your Qualytics permissions. Review the tool steps and resulting platform changes, particularly for operations that affect shared assets or external systems.

  • Run operations: Trigger sync, profile, scan, export, or materialize operations and poll for completion.
  • Promote assets: Copy computed tables, computed files, computed fields, and quality checks across datastores from chat.
  • Send notifications: Post alerts to Slack, Microsoft Teams, Email, Webhook, or PagerDuty.
  • Create tickets: Open issues in Jira or ServiceNow, optionally linked to a specific anomaly.
  • Manage tags: Apply, remove, or replace tags on datastores, containers, fields, and quality checks. The global tag catalog (list, create, update, delete) is managed through separate tools (list_tags, describe_tag, create_tag, update_tag, delete_tag); catalog mutations require the Manager or Admin role.
  • Manage check templates: List, create, update, or delete workspace-wide quality check templates (mutations require Manager or Admin role).

Tool Step Labels

When AgentQ processes a request, each action appears as an expandable step in the response. The labels correspond to specific platform actions:

Step Label What It Does
Search Queries across datastores, containers, and fields
List Quality Checks Retrieves existing checks on a container
List Check Specifications Fetches available rule types and their schemas
Create Quality Check Creates a new quality check rule
Update Quality Check Modifies an existing quality check
Quality Scores Retrieves 8-dimension quality scores
Get Insights Retrieves daily metrics time series
Operation Insights Retrieves historical operation data
Describe Anomaly Gets full details for a specific anomaly
Workflow Executes a guided multi-step workflow

Available Tools

AgentQ and external MCP clients share the same tool set. The available tools are consistent across sessions, while access to their data and actions remains subject to your Qualytics permissions.

The list below is a high-level index of capabilities. For exact parameter schemas (names, types, required vs optional), query the live MCP /tools endpoint or refer to the AgentQ API reference.

Exploration

Tool Description
list_datastores List datastores with optional name and tag filters.
list_containers List containers within a datastore with optional name filtering.
list_fields List fields within a container, including profile metadata (min, max, null count, distinct count).
global_search Search across all datastores, containers, and fields by name. Returns ranked matches.
preview_query Run a SELECT query against a JDBC datastore and return the results as a markdown table. Statements that change data or database objects are blocked.

Quality Checks

Tool Description
list_quality_check_specs List available quality check rule types and their JSON schemas.
list_quality_checks List quality checks defined on a container.
create_quality_check Create a new quality check rule.
update_quality_check Update an existing quality check.
list_quality_check_templates List available global quality check templates.
create_quality_check_template Create a new global quality check template. Requires Manager or Admin role.
update_quality_check_template Update an existing quality check template. Requires Manager or Admin role.
delete_quality_check_template Delete a quality check template. Requires Manager or Admin role.

Data Transformation

Tool Description
create_computed_table Create a computed table in a JDBC datastore.
create_computed_file Create a computed file in a file-based (DFS) datastore such as S3, ADLS, or GCS.
create_computed_join Create a cross-datastore join.
create_computed_field Add a derived or type-cast field to an existing container.

Anomalies

Tool Description
list_anomalies List anomalies, filterable by datastore, container, and status.
anomaly_describe Get full details for a single anomaly, including the failed check, affected field, sample values, and AI-generated description.

Insights & Scores

Tool Description
quality_scores Retrieve quality scores across the 8 dimensions (completeness, coverage, conformity, consistency, precision, timeliness, volumetrics, accuracy).
get_insights Retrieve daily time-series metrics for an asset (anomaly counts, score changes, scan coverage).
operation_insights Retrieve historical operation data (types, durations, record counts, completion statuses).

Operations

Tool Description
run_sync Trigger a sync operation on a datastore.
run_profile Trigger a profile operation on a container.
run_scan Trigger a scan operation on a container.
run_export Trigger an export operation.
run_materialize Trigger a materialize operation on a computed asset.
get_operation_status Poll the status of a running operation. AgentQ uses this to wait for terminal state before proceeding to dependent steps.

Notifications, Tickets, and Tags

Tool Description
send_notification Send an alert through a configured channel: Slack, Microsoft Teams, Email, Webhook, or PagerDuty.
create_ticket Create a ticket in Jira or ServiceNow, optionally linked to a specific anomaly.
list_integrations List configured notification and ticketing integrations.
manage_tags Apply, remove, or replace tags on datastores, containers, fields, and quality checks.
list_tags List global tags with optional filtering by name, type, or category.
describe_tag Get the details of a single global tag.
create_tag Create a new global tag. Requires Manager or Admin role.
update_tag Update an existing global tag. Requires Manager or Admin role.
delete_tag Delete a global tag. Requires Manager or Admin role.

Guided Workflows

Workflow tools execute multi-step guided processes for complex tasks. Each returns a structured AgentResponse with step-by-step results.

Tool Description
workflow_analyze_trends Analyze quality score trends and anomaly volume patterns over time.
workflow_investigate_anomaly Generate an AI-assisted investigation with suggested causes, business impact, and remediation steps.
workflow_interpret_quality_scores Interpret 8-dimension quality scores in business terms, highlighting areas for improvement.
workflow_generate_quality_check Generate and create a quality check from a natural language business rule.
workflow_transform_dataset Create a computed asset (table, file, or join) from a natural-language description.

Connecting External Clients

For step-by-step instructions on connecting ChatGPT, Claude Desktop, Cursor, and other MCP-compatible clients to the Qualytics MCP server, see Connecting External AI Clients.

For example conversations showing AgentQ in use, see Conversations, Responses & Context.