80 posts tagged with "stackql"

StackQL Studios has joined the Linux Foundation and the Agentic AI Foundation (AAIF) as a Silver Member.

As agentic AI moves from experimentation toward production workloads, we believe the infrastructure layer matters. Open, interoperable standards will be essential for AI agents to work reliably across cloud providers, data platforms, and enterprise systems.

StackQL provides a SQL-based interface for querying, provisioning, and managing cloud infrastructure across providers. Our work on the StackQL MCP Server brings this capability to AI agents through the Model Context Protocol, enabling agents to interact with cloud resources using natural language while maintaining the governance and auditability that production systems require.

Joining AAIF aligns with our long-standing commitment to open-source infrastructure tooling. We look forward to contributing to the foundation's work on MCP, agent runtimes, and the broader ecosystem of standards that will shape how AI agents interact with the systems they manage.

More information about the Agentic AI Foundation is available at aaif.io.

We've released a major update to the StackQL Google provider with a new service, enhanced AI/ML capabilities, and improvements across 177 service files.

New Service: Speech-to-Text v2​

The speechv2 service brings Cloud Speech-to-Text API v2 to StackQL with 6 resources:

Key features include support for multiple audio encodings (WAV, FLAC, MP3, OGG, WebM, MP4/AAC), translation capabilities, denoiser config, and KMS encryption support.

Vertex AI / AI Platform​

The largest update in this release with 87,000+ line changes introduces powerful new RAG and evaluation capabilities:

• RAG Resources: rag_corpora, rag_files, rag_engine_config for Retrieval-Augmented Generation
• Conversational AI: New chat resource
• Model Evaluation: evaluation_sets and evaluation_items for systematic model assessment
• New Resources: science, invoke, and openapi resources
• Performance: Enhanced cache_config for caching configurations

Discovery Engine​

Major enhancements (50,000+ line changes) for search and conversational AI:

• New assistants resource
• New sitemaps resource for site search
• New custom_models resource
• Enhanced sessions and answers for conversational search
• New authorized_views and authorized_view_sets for access control

Contact Center AI Insights​

Quality assurance and analytics improvements (20,000+ line changes):

• New qa_questions and qa_question_tags for quality assurance workflows
• New analysis_rules resource
• New segments resource
• New authorized_views with IAM policy support
• New datasets and views resources

BigQuery​

Enhanced governance and access control (18,000+ line changes):

• New routines_iam_policies for stored procedure/function IAM
• Enhanced row_access_policies

Healthcare API​

Expanded metrics and data mapping (15,000+ line changes):

• New data_mapper_workspaces_iam_policies
• Enhanced metrics: hl7_v2_store_metrics, dicom_store_metrics, series_metrics, study_metrics
• New instances_storage_info resource

Cloud Spanner​

Backup and security enhancements (14,000+ line changes):

• New backup_schedules with IAM support
• New databases_split_points resource
• New database_roles with IAM policies

Cloud SQL Admin​

New integration and management features (12,000+ line changes):

• New instances_entra_id_certificate for Microsoft Entra ID integration
• New instances_disk_shrink_config
• New instances_latest_recovery_time

GKE On-Prem​

Enhanced IAM across VMware and Bare Metal clusters (9,000+ line changes):

• Enhanced VMware cluster resources with IAM policies
• Enhanced Bare Metal cluster resources with IAM policies
• New vmware_node_pools and bare_metal_node_pools with IAM

Developer Connect​

Git integration improvements (3,500+ line changes):

• New git_repository_links_git_refs resource
• New users_self and users_access_token resources
• New token resources: read_token, read_write_token

Text-to-Speech​

Enhanced voices and text resources with new capabilities.

Get Started​

Update to the latest Google provider:

stackql registry pull google

Let us know your thoughts! Visit us and give us a star on GitHub.

Window functions and Common Table Expressions (CTEs) are now generally available in StackQL. These features work with both the embedded SQLite backend and PostgreSQL backend.

Window Functions​

Window functions allow you to perform calculations across sets of rows related to the current row. Supported functions include:

• Ranking: ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
• Offset: LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE(), NTH_VALUE()
• Distribution: PERCENT_RANK(), CUME_DIST()
• Aggregates as window functions: SUM(), COUNT(), AVG(), etc. with OVER clause

Example: Ranking Contributors​

SELECT
    login,
    contributions,
    DENSE_RANK() OVER (ORDER BY contributions DESC) as rank
FROM github.repos.contributors
WHERE owner = 'stackql' AND repo = 'stackql';

Example: Running Totals​

SELECT
    login,
    contributions,
    SUM(contributions) OVER (ORDER BY contributions DESC) as running_total,
    ROUND(100.0 * contributions / SUM(contributions) OVER (), 2) as pct_of_total
FROM github.repos.contributors
WHERE owner = 'stackql' AND repo = 'stackql';

Common Table Expressions (CTEs)​

CTEs let you define temporary named result sets using the WITH clause. This simplifies complex queries by breaking them into logical components.

Example: Aggregating Across Multiple Resources​

WITH all_contributors AS (
    SELECT login, contributions
    FROM github.repos.contributors
    WHERE owner = 'stackql' AND repo = 'stackql'
    UNION ALL
    SELECT login, contributions
    FROM github.repos.contributors
    WHERE owner = 'stackql' AND repo = 'stackql-deploy'
)
SELECT
    DENSE_RANK() OVER (ORDER BY SUM(contributions) DESC) as rank,
    login,
    SUM(contributions) as total_contributions
FROM all_contributors
GROUP BY login
ORDER BY total_contributions DESC;

Documentation​

Full documentation is available:

• Window Functions
• WITH (CTEs)
• SELECT

Let us know your thoughts! Visit us and give us a star on GitHub.

We've published a Claude Skill for StackQL provider development. It provides Claude with the context needed to help you build providers using the any-sdk library (interface used by StackQL to interact with the cloud providers).

What's in the Skill​

The skill covers the full provider development workflow:

• Provider document structure - The provider.yaml schema and service document layout
• OpenAPI extensions - All x-stackQL-* extensions including x-stackQL-resources, x-stackQL-config, x-stackQL-objectKey, and others
• Resource and method definitions - How to define resources, map methods to operations, and configure responses
• SQL verb mappings - Connecting REST operations to SELECT, INSERT, UPDATE, and DELETE
• Authentication patterns - API keys, service accounts, OAuth, and custom auth schemes
• Pagination handling - Request/response token configuration
• Response processing - JSONPath extraction, transformations, and schema handling

Download​

Grab the skill from the any-sdk repository:

Download stackql-provider-development.md

Usage​

With the skill loaded, you can ask Claude things like:

• "Create a provider definition for this API"
• "How do I map this endpoint to a SELECT operation?"
• "What's the correct objectKey syntax for this nested response?"
• "Help me configure pagination for this API"

Claude will have the full context of StackQL's OpenAPI extensions and provider patterns to work from.

Example​

Here's a sample interaction after loading the skill:

You: I have an API endpoint GET /users that returns {"data": {"users": [...]}}. 
     How do I set up the resource?

Claude: For that response structure, you'd configure the method like this:

methods:
  list:
    operation:
      $ref: '#/paths/~1users/get'
    response:
      mediaType: application/json
      openAPIDocKey: '200'
      objectKey: $.data.users

The objectKey uses JSONPath to extract the users array from the nested response.

Resources​

• StackQL Provider Registry
• any-sdk documentation
• StackQL documentation

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pystackql now includes a markdownkv output format optimized for LLM processing of control plane and data plane data from cloud providers.

Background​

Recent research from ImprovingAgents.com tested 11 data formats to determine which ones LLMs parse most accurately. Using 1,000 synthetic employee records and 1,000 randomized queries, they measured how well different formats preserved data integrity through LLM processing.

The results:

Markdown-KV showed a 37% improvement over CSV and 16 percentage points over JSON. The tradeoff: it uses approximately 2.7x more tokens than CSV.

What is Markdown-KV?​

Markdown-KV uses hierarchical markdown headers with code blocks for key-value pairs:

# Query Results

## Record 1

id: i-1234567890abcdef0
name: prod-web-01
region: us-east-1
instance_type: t3.large
state: running


## Record 2

id: i-0987654321fedcba0
name: staging-web-01
region: us-west-2
instance_type: t3.medium
state: stopped

The format combines clear hierarchy, explicit key-value pairs, and readability for both humans and LLMs.

Usage​

from pystackql import StackQL

stackql = StackQL()

# Query with Markdown-KV output
result = stackql.execute(
    """
    SELECT instanceId, instanceType, state, availabilityZone 
    FROM aws.ec2.instances 
    WHERE region = 'us-east-1'
    """,
    output='markdownkv'
)

# Use with LLMs
response = llm_client.complete(
    f"Identify instances that should be stopped:\n\n{result}"
)

Works in server mode too:

stackql = StackQL(server_mode=True)

result = stackql.execute(
    "SELECT name, region, encryption FROM google.storage.buckets WHERE project = 'my-project'",
    output='markdownkv'
)

When to Use It​

Markdown-KV is useful when:

• Feeding infrastructure data to LLMs for analysis, security reviews, or recommendations
• Building RAG pipelines that need to accurately retrieve and reason about infrastructure
• Accuracy matters more than token efficiency (infrastructure decisions typically do)
• Query results are focused datasets (most StackQL queries are)

The token cost is a real tradeoff, but infrastructure queries typically return targeted result sets, not massive datasets. When you're asking an LLM to analyze your production environment, accuracy matters.

Getting Started​

Update pystackql:

pip install --upgrade pystackql

Add output='markdownkv' to your execute calls or in the StackQL object instantiation:

result = stackql.execute(query, output='markdownkv')

Resources​

• ImprovingAgents.com research on LLM data formats
• pystackql v3.8.2 changelog
• pystackql documentation
• StackQL documentation

The Markdown-KV output format is available in pystackql v3.8.2 and later.

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