dbt-labs/dbt-mcp
Sep 2025 – Jan 2026
3 Months active
Languages Used
PythonYAMLMarkdown
Technical Skills
Agent DevelopmentAutomationBigQueryGoogle CloudPythonPython Development
Muizz Lateef
PROFILE
Muizz Lateef
Lateef Muizz contributed to the dbt-labs/dbt-mcp repository by building features that enhanced local development, automated code generation, and improved data graph visibility. He developed tools for integrating Google ADK with dbt-core and automated dbt code generation, reducing setup friction and accelerating workflows. His work included implementing GraphQL-based APIs for source metadata discovery and lineage traversal, as well as adding dimension metadata support in the Semantic Layer. Using Python, GraphQL, and asynchronous programming, Lateef ensured robust testing and documentation accompanied each feature, demonstrating depth in both backend and integration engineering while aligning with best practices for maintainability and user onboarding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
5
Lines of code
2,596
Activity Months3
Your Network
22 peopleWork History
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for dbt-mcp: Delivered two high-value capabilities that enhance lineage visibility and semantic layer richness, supported by thorough testing and documentation. Key changes include a new get_lineage tool enabling complete lineage traversal across all dbt resources via the Discovery API's lineage endpoint, and dimension metadata support added to the get_dimensions response in the Semantic Layer. These updates reduce manual governance effort, enable faster impact analysis, and align with MCP best practices. Tech stack highlights include the Discovery API lineage endpoint, GraphQL enhancements, and comprehensive unit/integration tests with documentation updates.
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for dbt-mcp: Delivered two high-value capabilities that enhance lineage visibility and semantic layer richness, supported by thorough testing and documentation. Key changes include a new get_lineage tool enabling complete lineage traversal across all dbt resources via the Discovery API's lineage endpoint, and dimension metadata support added to the get_dimensions response in the Semantic Layer. These updates reduce manual governance effort, enable faster impact analysis, and align with MCP best practices. Tech stack highlights include the Discovery API lineage endpoint, GraphQL enhancements, and comprehensive unit/integration tests with documentation updates.
January 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) focused on expanding data graph visibility in dbt-mcp by delivering a source metadata discovery capability and aligning the MCP-Discovery API with source metadata. The work includes a GraphQL-based get_all_sources tool, a new SourcesFetcher with pagination, and integration into the existing discovery and lineage frameworks. This also fixed naming inconsistencies in source filters and added tests to ensure correctness and stability.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) focused on expanding data graph visibility in dbt-mcp by delivering a source metadata discovery capability and aligning the MCP-Discovery API with source metadata. The work includes a GraphQL-based get_all_sources tool, a new SourcesFetcher with pagination, and integration into the existing discovery and lineage frameworks. This also fixed naming inconsistencies in source filters and added tests to ensure correctness and stability.
September 2025
2 Commits • 2 Features
Sep 1, 2025Month: 2025-09 | Repository: dbt-labs/dbt-mcp Summary: Focused delivery of two strategic features enabling local development and automated code generation, accompanied by improved documentation to accelerate adoption. No major bugs reported or escalated this month; maintenance work remained steady to support feature rollout and code quality. Key outcomes: - Delivered two major features with clear business value: local Google ADK integration for dbt-core with BigQuery, and a new dbt-codegen toolset for automated code generation. These efforts collectively reduce setup friction, enable broader access to BigQuery data, and accelerate development workflows for customers and internal teams. - Strengthened documentation and onboarding around local development and BigQuery workflows to shorten time-to-value for users. Technical and collaboration notes: - Commits referenced below demonstrate end-to-end work from local environment integration to tooling enhancements, with cross-team collaboration signals in commit metadata. - Technologies emphasized: dbt-core, Google ADK, Google BigQuery, local development workflows, and code generation tooling.
2 Commits • 2 Features
Sep 1, 2025Month: 2025-09 | Repository: dbt-labs/dbt-mcp Summary: Focused delivery of two strategic features enabling local development and automated code generation, accompanied by improved documentation to accelerate adoption. No major bugs reported or escalated this month; maintenance work remained steady to support feature rollout and code quality. Key outcomes: - Delivered two major features with clear business value: local Google ADK integration for dbt-core with BigQuery, and a new dbt-codegen toolset for automated code generation. These efforts collectively reduce setup friction, enable broader access to BigQuery data, and accelerate development workflows for customers and internal teams. - Strengthened documentation and onboarding around local development and BigQuery workflows to shorten time-to-value for users. Technical and collaboration notes: - Commits referenced below demonstrate end-to-end work from local environment integration to tooling enhancements, with cross-team collaboration signals in commit metadata. - Technologies emphasized: dbt-core, Google ADK, Google BigQuery, local development workflows, and code generation tooling.
September 2025
Activity
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Quality Metrics
Correctness96.0%
Maintainability84.0%
Architecture92.0%
Performance76.0%
AI Usage36.0%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
API DevelopmentAPI IntegrationAgent DevelopmentAsynchronous ProgrammingAutomationBigQueryFull Stack DevelopmentGoogle CloudGraphQLPythonPython DevelopmentSoftware DevelopmentTestingUnit Testingdbt
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