{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_ana-by-textql","slug":"ana-by-textql","name":"Ana by TextQL","type":"product","url":"https://ana.textql.com","page_url":"https://unfragile.ai/ana-by-textql","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_ana-by-textql__cap_0","uri":"capability://data.processing.analysis.natural.language.to.sql.query.translation.with.privacy.preserving.execution","name":"natural language to sql query translation with privacy-preserving execution","description":"Converts natural language questions into SQL queries that execute against user-controlled databases without transmitting raw data to external servers. The system maintains schema awareness of connected databases and generates syntactically correct SQL for multiple database backends (PostgreSQL, MySQL, etc.), then executes queries locally and returns only aggregated results or visualizations rather than raw datasets.","intents":["Query my database using conversational language without writing SQL manually","Analyze sensitive data without exposing it to cloud-based analytics platforms","Generate ad-hoc reports from structured data using natural language prompts","Explore datasets interactively without needing SQL expertise"],"best_for":["Privacy-conscious organizations in regulated industries (healthcare, finance, legal)","Solo data analysts and small teams with existing on-premise database infrastructure","Teams evaluating AI analytics before committing to enterprise solutions","Developers building internal analytics tools with strict data residency requirements"],"limitations":["Requires users to maintain and manage their own database infrastructure — no managed hosting provided","SQL generation accuracy depends on schema complexity; highly normalized or non-standard schemas may produce incorrect queries","Limited to databases with SQL interfaces; NoSQL, data lakes, and proprietary formats require custom connectors","No built-in query optimization or cost estimation for expensive analytical queries against large datasets","Context window limitations may prevent understanding of very large schemas with hundreds of tables"],"requires":["Active database connection (PostgreSQL, MySQL, or compatible SQL database)","Network connectivity between Ana client and database server","Database user account with SELECT permissions on target tables","Sufficient database schema documentation or introspection capabilities for accurate NL-to-SQL mapping"],"input_types":["natural language text queries","database connection credentials","schema metadata"],"output_types":["SQL query text","query results (structured rows)","aggregated metrics","visualization-ready datasets"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_1","uri":"capability://data.processing.analysis.database.schema.introspection.and.context.management.for.query.generation","name":"database schema introspection and context management for query generation","description":"Automatically discovers and maintains awareness of database schema structure (tables, columns, data types, relationships) to inform accurate natural language to SQL translation. The system introspects connected databases to build a queryable schema representation, manages schema updates, and selectively includes relevant schema context in LLM prompts to improve query generation accuracy while staying within token budgets.","intents":["Automatically detect available tables and columns without manual schema documentation","Ensure NL-to-SQL translation understands relationships between tables for JOIN generation","Keep schema context current as database structure evolves","Reduce hallucination in SQL generation by grounding queries in actual schema metadata"],"best_for":["Teams with frequently-changing database schemas that need automatic discovery","Organizations migrating from manual BI tools to conversational analytics","Developers building multi-tenant analytics platforms with dynamic schemas"],"limitations":["Schema introspection adds latency (typically 1-5 seconds) on first connection and after schema changes","Cannot infer semantic meaning from column names alone; ambiguous naming (e.g., 'id', 'value') may produce incorrect queries","Foreign key relationships must be explicitly defined in database; inferred relationships are not supported","Large schemas (1000+ tables) may exceed token budgets for LLM context, requiring manual schema selection","Schema introspection requires elevated database permissions (INFORMATION_SCHEMA access) that may not be available in restricted environments"],"requires":["Database with accessible INFORMATION_SCHEMA or equivalent metadata tables","Database user with SELECT permissions on system catalogs","Network connectivity to database for real-time schema discovery"],"input_types":["database connection parameters","optional schema filters or table allowlists"],"output_types":["structured schema metadata (tables, columns, types, constraints)","relationship graph (foreign keys, indexes)","schema context for LLM prompts"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_2","uri":"capability://code.generation.editing.multi.database.backend.support.with.dialect.aware.sql.generation","name":"multi-database backend support with dialect-aware sql generation","description":"Generates syntactically correct SQL queries for multiple database systems (PostgreSQL, MySQL, SQLite, etc.) by detecting target database type and applying dialect-specific syntax rules. The system translates abstract query intent into database-specific SQL, handling differences in function names, date handling, string operations, and aggregation syntax across backends.","intents":["Query different database systems using the same natural language interface","Migrate analytics queries between database platforms without manual rewriting","Support heterogeneous data infrastructure with multiple database backends","Generate correct SQL syntax for the specific database in use"],"best_for":["Organizations with polyglot database architectures (mix of PostgreSQL, MySQL, SQLite, etc.)","Teams migrating between database platforms and needing consistent query interfaces","Enterprises with legacy databases alongside modern data warehouses"],"limitations":["Dialect support is limited to major open-source and commercial databases; proprietary or niche databases may not be supported","Advanced database-specific features (window functions, CTEs, JSON operators) may not translate correctly across all dialects","Performance characteristics vary significantly between databases; generated queries may be inefficient on some backends without manual tuning","Dialect differences in NULL handling, type coercion, and string comparison may produce unexpected results when switching databases"],"requires":["Connection to supported database backend (PostgreSQL 10+, MySQL 5.7+, SQLite 3.8+, or equivalent)","Database dialect explicitly specified or auto-detected from connection metadata"],"input_types":["natural language query","target database type identifier"],"output_types":["dialect-specific SQL query text","query results from target database"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_3","uri":"capability://image.visual.data.visualization.generation.from.query.results.with.customization","name":"data visualization generation from query results with customization","description":"Transforms SQL query results into visual representations (charts, graphs, tables) with configurable styling and layout options. The system analyzes result schema and data characteristics to recommend appropriate visualization types, generates visualization specifications, and renders interactive or static visualizations based on user preferences and output format requirements.","intents":["Automatically visualize query results without manual chart configuration","Create presentation-ready visualizations for reports and dashboards","Explore data patterns through multiple visualization types","Customize chart appearance (colors, labels, axes) for branding or clarity"],"best_for":["Analysts who need quick exploratory visualizations without design expertise","Teams generating automated reports from database queries","Stakeholders presenting data insights to non-technical audiences"],"limitations":["Visualization capabilities and customization depth are unclear from available documentation; may be limited compared to specialized BI tools like Tableau or Power BI","Automatic visualization type selection may not match analyst intent for complex or domain-specific data","Limited support for advanced visualization types (3D, geospatial, network graphs) compared to dedicated visualization libraries","Export formats and quality may be constrained; unclear if high-resolution output suitable for print is supported","No apparent support for interactive dashboard creation or multi-chart layouts"],"requires":["Query results with structured data (rows and columns)","Supported data types for visualization (numeric, categorical, temporal)"],"input_types":["SQL query results (structured tabular data)","optional visualization preferences or type hints"],"output_types":["interactive charts (web-based)","static visualizations (PNG, SVG, PDF)","visualization specifications (JSON, Vega-Lite, etc.)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_4","uri":"capability://text.generation.language.conversational.multi.turn.query.refinement.with.context.preservation","name":"conversational multi-turn query refinement with context preservation","description":"Maintains conversation context across multiple natural language queries, allowing users to refine, filter, or expand previous queries through follow-up questions. The system preserves previous query results, schema context, and user intent across conversation turns, enabling iterative data exploration without re-specifying full context for each question.","intents":["Ask follow-up questions that build on previous query results without restating context","Iteratively refine queries based on initial results and insights","Explore data through conversational dialogue rather than isolated queries","Maintain analysis context across multiple analytical questions"],"best_for":["Exploratory data analysts performing iterative analysis sessions","Non-technical users who benefit from conversational refinement","Teams conducting ad-hoc investigations requiring multiple related queries"],"limitations":["Context window limitations may prevent preserving very long conversation histories; older queries may be dropped from context","Ambiguous follow-up questions may be misinterpreted without explicit reference to previous context","No apparent support for branching conversations or exploring multiple analytical paths simultaneously","Context preservation adds latency to each query as previous conversation must be re-processed by LLM","No persistent conversation storage; sessions are lost when connection closes unless explicitly saved"],"requires":["Active conversation session with maintained connection","Sufficient LLM context window to preserve conversation history (typically 4K-8K tokens)"],"input_types":["natural language follow-up questions","optional explicit context references"],"output_types":["refined SQL queries","query results","conversational responses explaining results"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_5","uri":"capability://code.generation.editing.local.llm.inference.option.with.privacy.first.model.selection","name":"local llm inference option with privacy-first model selection","description":"Supports running language models locally on user infrastructure rather than relying on cloud-based API calls, enabling complete data privacy by keeping both data and model inference on-premise. The system abstracts LLM provider selection, allowing users to choose between cloud APIs (OpenAI, Anthropic) and local models (Ollama, LLaMA, Mistral) with consistent query generation interfaces.","intents":["Run analytics without sending any data or queries to external cloud services","Maintain complete data sovereignty for regulated industries with strict data residency requirements","Reduce API costs by using open-source models instead of commercial LLM APIs","Ensure query generation logic remains under organizational control"],"best_for":["Organizations in regulated industries (healthcare, finance, government) with strict data residency mandates","Teams with high-volume query workloads where API costs are prohibitive","Enterprises requiring complete infrastructure control and no external dependencies","Developers building analytics tools for air-gapped or offline environments"],"limitations":["Local model inference requires significant computational resources (GPU or high-end CPU); may be impractical for small teams or resource-constrained environments","Open-source models typically have lower SQL generation accuracy than commercial LLMs (GPT-4, Claude); query correctness may suffer","Setup and maintenance of local LLM infrastructure adds operational complexity compared to API-based approaches","Model selection and tuning requires technical expertise; wrong model choice may result in poor query generation","Unclear if local inference option is actually implemented or if it's a planned feature; documentation may not reflect current capabilities"],"requires":["Local LLM runtime (Ollama, vLLM, or similar) installed and configured","Sufficient GPU VRAM (8GB-24GB depending on model size) or high-end CPU for inference","Compatible open-source model (Mistral, LLaMA 2, CodeLLaMA, or similar) with SQL generation capabilities","Network connectivity between Ana client and local LLM service"],"input_types":["natural language query","database schema context","optional model configuration parameters"],"output_types":["SQL query generated by local model","query results"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_6","uri":"capability://data.processing.analysis.query.result.caching.and.incremental.refresh.for.performance.optimization","name":"query result caching and incremental refresh for performance optimization","description":"Caches previously executed query results and reuses them for identical or similar queries, reducing database load and latency for repeated analytical questions. The system detects query similarity, manages cache invalidation based on data freshness requirements, and supports incremental updates when underlying data changes, balancing performance with result accuracy.","intents":["Reduce latency for repeated queries without re-executing against the database","Minimize database load from redundant analytical queries","Provide fast exploratory analysis by reusing cached results for similar questions","Support offline analysis by caching results for later exploration"],"best_for":["Teams with high-volume analytical workloads where database performance is a bottleneck","Exploratory analysis sessions where similar queries are executed multiple times","Organizations with expensive database infrastructure wanting to minimize query volume","Analysts needing fast iteration on data exploration"],"limitations":["Cache invalidation strategy is unclear; may return stale results if data changes between queries","No apparent support for cache TTL configuration or manual invalidation triggers","Query similarity detection may be imprecise, leading to incorrect cache hits for semantically different queries","Cache storage location and size limits are not documented; unclear if local disk or in-memory","No visibility into cache hit rates or performance impact metrics"],"requires":["Local storage for query result cache (disk or memory)","Mechanism to detect query similarity or exact matches"],"input_types":["SQL queries","optional cache configuration (TTL, invalidation rules)"],"output_types":["cached query results","cache metadata (age, hit status)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_7","uri":"capability://data.processing.analysis.export.and.integration.with.downstream.analytics.and.reporting.tools","name":"export and integration with downstream analytics and reporting tools","description":"Exports query results and visualizations in multiple formats (CSV, JSON, Parquet, etc.) for integration with external analytics, BI, and reporting tools. The system supports standard data interchange formats and may provide direct connectors to popular tools, enabling Ana to function as a query layer feeding into existing analytics pipelines.","intents":["Export query results for use in Excel, Python, R, or other analysis tools","Feed Ana-generated insights into existing BI dashboards and reporting systems","Integrate Ana queries into automated data pipelines and ETL workflows","Share analysis results with stakeholders using standard data formats"],"best_for":["Teams with existing analytics stacks wanting to add conversational query capabilities","Analysts who need to combine Ana insights with other analytical tools","Organizations building data pipelines that require SQL query abstraction","Stakeholders sharing analysis results across different tools and platforms"],"limitations":["Supported export formats and integrations are not clearly documented; unclear which tools are directly supported","No apparent support for real-time streaming exports or incremental data delivery","Export performance may be limited by data volume; large result sets may be slow or memory-intensive to export","No built-in scheduling or automation for recurring exports; likely requires manual export or external orchestration","Data transformation during export (formatting, filtering, aggregation) capabilities are unclear"],"requires":["Query results to export","Target system or format specification","Sufficient storage or network bandwidth for export"],"input_types":["query results (structured data)","export format specification","optional target system credentials"],"output_types":["CSV files","JSON data","Parquet files","direct integration with external tools"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_8","uri":"capability://safety.moderation.access.control.and.multi.user.collaboration.with.audit.logging","name":"access control and multi-user collaboration with audit logging","description":"Manages user permissions for database access, query execution, and result visibility while maintaining audit trails of analytical activities. The system enforces role-based access control (RBAC) or attribute-based access control (ABAC) to restrict which users can query which databases and tables, and logs all query execution and result access for compliance and security monitoring.","intents":["Control which team members can access sensitive databases and tables","Enforce data governance policies by restricting analytical access based on user roles","Maintain audit trails for compliance with regulatory requirements (HIPAA, GDPR, SOC 2)","Enable secure multi-user analytics without exposing sensitive data to unauthorized users"],"best_for":["Organizations in regulated industries requiring strict access control and audit trails","Teams with sensitive data requiring granular permission management","Enterprises needing to demonstrate compliance with data governance policies","Multi-tenant analytics platforms requiring user isolation"],"limitations":["Access control implementation details are not documented; unclear if RBAC, ABAC, or other models are used","No apparent support for row-level security (RLS) or column-level masking; access control may be at table level only","Audit logging scope and retention policies are not specified; unclear what events are logged and for how long","Integration with enterprise identity providers (LDAP, SAML, OAuth) is not documented","No apparent support for dynamic access control based on data sensitivity or classification"],"requires":["User authentication mechanism (local accounts, LDAP, SAML, or OAuth)","Role or permission definitions for users","Audit logging infrastructure (local storage or external system)"],"input_types":["user credentials","role or permission assignments","optional audit configuration"],"output_types":["access control decisions (allow/deny)","audit logs (query execution, result access)","compliance reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ana-by-textql__cap_9","uri":"capability://text.generation.language.error.handling.and.query.validation.with.user.friendly.explanations","name":"error handling and query validation with user-friendly explanations","description":"Detects invalid or problematic SQL queries before execution, provides clear error messages explaining why queries failed, and suggests corrections or alternative approaches. The system validates query syntax, checks for schema mismatches, identifies performance issues, and translates database error messages into user-friendly explanations that guide users toward correct queries.","intents":["Understand why a natural language query couldn't be translated to valid SQL","Get suggestions for fixing ambiguous or incorrect queries","Avoid executing expensive or problematic queries that would fail or harm performance","Learn SQL concepts through error explanations and suggestions"],"best_for":["Non-technical users learning to use conversational analytics","Teams wanting to prevent invalid queries from reaching the database","Analysts debugging why natural language questions produce unexpected results","Organizations wanting to improve query generation accuracy through user feedback"],"limitations":["Error message quality depends on LLM's ability to explain SQL concepts; explanations may be technical or unclear for non-experts","Query validation may be overly conservative, rejecting valid queries due to false positives","Suggestions for query correction may not match user intent; users may need to rephrase questions","No apparent support for learning from user corrections to improve future query generation","Performance issue detection may be limited; expensive queries might not be flagged before execution"],"requires":["SQL query to validate","Database schema for validation","LLM for generating explanations"],"input_types":["SQL query text","database schema","optional database error messages"],"output_types":["validation status (valid/invalid)","error messages","suggested corrections","performance warnings"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Active database connection (PostgreSQL, MySQL, or compatible SQL database)","Network connectivity between Ana client and database server","Database user account with SELECT permissions on target tables","Sufficient database schema documentation or introspection capabilities for accurate NL-to-SQL mapping","Database with accessible INFORMATION_SCHEMA or equivalent metadata tables","Database user with SELECT permissions on system catalogs","Network connectivity to database for real-time schema discovery","Connection to supported database backend (PostgreSQL 10+, MySQL 5.7+, SQLite 3.8+, or equivalent)","Database dialect explicitly specified or auto-detected from connection metadata","Query results with structured data (rows and columns)"],"failure_modes":["Requires users to maintain and manage their own database infrastructure — no managed hosting provided","SQL generation accuracy depends on schema complexity; highly normalized or non-standard schemas may produce incorrect queries","Limited to databases with SQL interfaces; NoSQL, data lakes, and proprietary formats require custom connectors","No built-in query optimization or cost estimation for expensive analytical queries against large datasets","Context window limitations may prevent understanding of very large schemas with hundreds of tables","Schema introspection adds latency (typically 1-5 seconds) on first connection and after schema changes","Cannot infer semantic meaning from column names alone; ambiguous naming (e.g., 'id', 'value') may produce incorrect queries","Foreign key relationships must be explicitly defined in database; inferred relationships are not supported","Large schemas (1000+ tables) may exceed token budgets for LLM context, requiring manual schema selection","Schema introspection requires elevated database permissions (INFORMATION_SCHEMA access) that may not be available in restricted environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ana-by-textql","compare_url":"https://unfragile.ai/compare?artifact=ana-by-textql"}},"signature":"XF+HIYpmjGqYVHxFNp02Xa2Eq3WefggUWROG+Ke40Nn6CmryHZODD6CF3dXaBIk02HNydNO8KHluZrcMEz83CA==","signedAt":"2026-06-19T17:49:56.628Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ana-by-textql","artifact":"https://unfragile.ai/ana-by-textql","verify":"https://unfragile.ai/api/v1/verify?slug=ana-by-textql","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}