{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_datalang","slug":"datalang","name":"DataLang","type":"product","url":"https://datalang.io","page_url":"https://unfragile.ai/datalang","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_datalang__cap_0","uri":"capability://code.generation.editing.natural.language.to.sql.query.generation.with.llm.based.translation","name":"natural-language-to-sql query generation with llm-based translation","description":"Converts plain English questions into executable SQL queries using large language models to parse user intent and map it to database schema. The system likely uses prompt engineering with schema context injection, where the LLM receives the target database's table definitions, column names, and relationships as part of the prompt, then generates syntactically valid SQL. This approach trades off query accuracy for accessibility, requiring human verification for complex analytical patterns.","intents":["I want to ask my database questions without writing SQL","I need to let non-technical team members query data independently","I want to reduce the time spent writing and debugging SQL queries for routine questions"],"best_for":["business analysts without SQL training","non-technical stakeholders needing ad-hoc data access","teams with IT support available for query validation"],"limitations":["LLM-generated queries frequently fail on complex multi-table joins with conditional logic, requiring manual SQL correction","Accuracy degrades significantly with domain-specific terminology and industry jargon not present in training data","No explicit reasoning or query plan explanation — users cannot understand why a query was generated a certain way","Context window limitations mean very large schemas or complex business logic may be truncated or misunderstood"],"requires":["Active database connection (PostgreSQL, MySQL, or Snowflake)","API key or authentication credentials for the target database","LLM API access (likely OpenAI or similar, depending on DataLang's backend)","Database schema must be discoverable and readable by the translation layer"],"input_types":["natural language text (English questions)"],"output_types":["SQL query string","query execution results (structured rows)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_1","uri":"capability://data.processing.analysis.multi.database.schema.discovery.and.context.injection","name":"multi-database schema discovery and context injection","description":"Automatically introspects connected databases (PostgreSQL, MySQL, Snowflake) to extract table definitions, column names, data types, and relationships, then injects this metadata into the LLM prompt context. This enables the LLM to generate contextually appropriate queries without manual schema definition. The system likely uses database-specific information schema queries (e.g., `information_schema.tables` in PostgreSQL) to build a normalized schema representation.","intents":["I want the system to automatically understand my database structure without manual configuration","I need the query generator to know about all available tables and columns","I want schema changes to be reflected automatically without re-configuration"],"best_for":["enterprises with multiple databases requiring unified query interface","teams that frequently add or modify database schemas","organizations wanting zero-configuration data access setup"],"limitations":["Schema discovery may timeout or fail on very large databases (1000+ tables) due to introspection overhead","Relationship inference is limited — foreign keys must be explicitly defined; implicit relationships are not detected","No support for dynamic schema changes mid-session; schema is cached at connection time","Sensitive column metadata (e.g., PII flags, encryption status) is not discovered and cannot inform query generation safety"],"requires":["Database user account with SELECT permissions on information_schema or equivalent","Network connectivity to target database","PostgreSQL 9.6+, MySQL 5.7+, or Snowflake account","Sufficient database permissions to read system catalogs"],"input_types":["database connection parameters (host, port, credentials, database name)"],"output_types":["normalized schema representation (table/column metadata)","relationship graph (foreign key mappings)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_2","uri":"capability://data.processing.analysis.query.execution.and.result.streaming.with.database.abstraction","name":"query execution and result streaming with database abstraction","description":"Executes generated SQL queries against the target database and streams results back to the user, abstracting away database-specific connection handling and result formatting. The system likely uses a database abstraction layer (e.g., SQLAlchemy-like pattern) to normalize connections across PostgreSQL, MySQL, and Snowflake, handling connection pooling, transaction management, and result serialization. Results are likely streamed or paginated to avoid memory overhead on large result sets.","intents":["I want to run a query and see results immediately without managing database connections","I need results formatted consistently regardless of which database I'm querying","I want to handle large result sets without loading everything into memory at once"],"best_for":["teams querying multiple database types and needing unified execution","analysts working with large datasets (millions of rows)","non-technical users who should not manage connection details"],"limitations":["Abstraction layer adds latency (typically 50-200ms per query) compared to direct database drivers","Result pagination or streaming may not preserve query semantics for ORDER BY or LIMIT clauses across pages","No built-in query timeout or resource limits — runaway queries can consume database resources","Error messages are abstracted and may not provide database-specific debugging information"],"requires":["Active database connection with valid credentials","Network connectivity to database (no local caching of data)","Database user with SELECT (and potentially INSERT/UPDATE/DELETE) permissions","Sufficient database resources to execute the query"],"input_types":["SQL query string","database connection parameters"],"output_types":["structured result set (rows and columns)","JSON or CSV formatted results","metadata (row count, execution time)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_3","uri":"capability://text.generation.language.conversational.query.refinement.with.multi.turn.dialogue","name":"conversational query refinement with multi-turn dialogue","description":"Maintains conversation context across multiple user questions, allowing users to ask follow-up questions that reference previous queries or results. The system likely stores conversation history (user questions, generated queries, results) and injects relevant context into subsequent LLM prompts, enabling the model to understand references like 'show me the top 5' or 'break that down by region' without re-stating the full context. This pattern is common in conversational AI systems using sliding-window context management.","intents":["I want to ask follow-up questions without repeating context","I want to refine a query iteratively based on initial results","I want the system to remember what we were analyzing in previous questions"],"best_for":["analysts performing exploratory data analysis with multiple related questions","business users iterating on queries to find insights","teams using DataLang for interactive data discovery sessions"],"limitations":["Context window is limited — very long conversations (50+ turns) may lose early context due to token budget constraints","No explicit memory of previous queries — context is inferred from conversation history, not stored persistently","Ambiguous follow-ups may be misinterpreted if context is insufficient (e.g., 'that' could refer to multiple previous results)","No session persistence — conversation history is lost if the session ends; users cannot resume previous analysis"],"requires":["Active session with maintained conversation history","LLM with sufficient context window (typically 4K+ tokens)","User to maintain conversation within a single session"],"input_types":["natural language follow-up questions","previous query results (implicit context)"],"output_types":["refined SQL query","query results","clarification prompts (if context is ambiguous)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_4","uri":"capability://code.generation.editing.query.validation.and.error.correction.with.user.feedback.loop","name":"query validation and error correction with user feedback loop","description":"Detects when generated SQL queries are syntactically invalid or semantically incorrect (e.g., referencing non-existent columns, invalid joins) and either auto-corrects them or prompts the user for clarification. The system likely executes queries in a dry-run or validation mode first, catches database errors, and feeds those errors back to the LLM with instructions to regenerate the query. This creates a feedback loop where the LLM learns from execution failures within a single session.","intents":["I want the system to catch query errors before they waste database resources","I want automatic correction of simple query mistakes","I want clear error messages explaining why a query failed"],"best_for":["non-technical users who cannot debug SQL errors themselves","teams wanting to prevent invalid queries from executing","organizations concerned about database performance impact of malformed queries"],"limitations":["Error correction works only for simple, obvious mistakes (typos, missing columns); complex logical errors are not detected","Feedback loop adds latency — each failed query requires re-generation and re-execution, potentially 2-3x slower than a correct query on first attempt","No learning across sessions — errors in one conversation do not improve query generation in future conversations","User feedback is not captured or used to improve the model; corrections are ephemeral"],"requires":["Database connection with query execution permissions","Error messages from database (must be parseable and informative)","LLM capable of understanding and correcting SQL syntax errors"],"input_types":["generated SQL query","database error message"],"output_types":["corrected SQL query","error explanation in natural language","validation status (valid/invalid)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_5","uri":"capability://text.generation.language.natural.language.result.interpretation.and.summarization","name":"natural language result interpretation and summarization","description":"Automatically generates natural language summaries and insights from query results, translating raw data into human-readable narratives. The system likely uses the LLM to analyze result sets and produce summaries like 'Revenue increased 15% month-over-month' or 'Top 3 customers account for 40% of sales', rather than just displaying raw rows. This adds a layer of semantic interpretation on top of query execution.","intents":["I want insights from data without manually analyzing rows","I want results explained in business terms, not raw SQL output","I want the system to highlight key findings automatically"],"best_for":["business executives and non-technical stakeholders","teams wanting automated insight generation from queries","organizations building data-driven dashboards with narrative explanations"],"limitations":["Summaries are generated by LLM and may be inaccurate or misleading if the model misinterprets data patterns","No statistical rigor — insights are narrative-based, not validated with confidence intervals or significance tests","Summaries are not reproducible; same data may generate different narratives on different runs due to LLM stochasticity","Large result sets (10,000+ rows) may be truncated or sampled, leading to incomplete or biased summaries"],"requires":["Query results in structured format (rows and columns)","LLM access for summarization","Result set small enough to fit in LLM context (typically <10K rows or <100K tokens)"],"input_types":["structured query results (rows and columns)"],"output_types":["natural language summary","key insights (bullet points)","narrative explanation"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_6","uri":"capability://safety.moderation.access.control.and.query.auditing.with.user.level.permissions","name":"access control and query auditing with user-level permissions","description":"Enforces row-level and column-level access control, ensuring users can only query data they are authorized to access. The system likely integrates with database-level permissions or implements an additional authorization layer that filters queries or results based on user identity. Query execution is logged for audit trails, tracking who queried what data and when, which is critical for compliance in regulated industries.","intents":["I want to ensure users only see data they are authorized for","I need audit logs of who accessed what data and when","I want to enforce data governance policies without manual SQL filtering"],"best_for":["enterprises with strict data governance requirements","regulated industries (healthcare, finance) requiring audit trails","organizations with sensitive data requiring fine-grained access control"],"limitations":["Row-level security requires database-level support (PostgreSQL RLS, Snowflake dynamic masking) or custom filtering logic, adding complexity","Column-level filtering may require query rewriting, which can be error-prone and add latency","Audit logs are not real-time; there may be delays in logging and retrieval","No built-in data masking or PII redaction — sensitive data in results is not automatically obscured"],"requires":["Database with native access control support (PostgreSQL RLS, Snowflake role-based access, MySQL user privileges)","User authentication system (LDAP, OAuth, SAML)","Audit logging infrastructure (database logs, external log aggregation)","Compliance requirements (HIPAA, GDPR, SOC 2) to justify the overhead"],"input_types":["user identity (username, role, group)","query request"],"output_types":["filtered query results","audit log entry","access denial (if unauthorized)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_7","uri":"capability://memory.knowledge.saved.queries.and.query.templates.with.reusability","name":"saved queries and query templates with reusability","description":"Allows users to save frequently-used natural language questions as templates, which can be reused with different parameters (e.g., 'Show me sales for [MONTH]' where MONTH is a variable). The system likely stores the mapping between natural language questions and generated SQL, enabling quick re-execution without regeneration. This reduces latency for common queries and provides a library of validated queries that have been manually verified.","intents":["I want to save queries I use frequently and run them again quickly","I want to create parameterized queries that work for different time periods or regions","I want a library of pre-validated queries that don't require LLM regeneration"],"best_for":["teams with recurring reporting needs","organizations wanting to build a library of trusted queries","analysts who run similar analyses on different data subsets"],"limitations":["Templates are static — if the database schema changes, saved queries may become invalid without re-validation","Parameter substitution is limited; complex conditional logic cannot be parameterized","No version control or change tracking for saved queries","Sharing saved queries across users requires manual export/import or centralized storage"],"requires":["Persistent storage for saved queries (database, file system, or cloud storage)","User authentication to associate queries with users","Query validation before saving (to ensure saved queries are correct)"],"input_types":["natural language question","parameter values (for parameterized queries)"],"output_types":["saved query reference","query results (from saved query execution)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_datalang__cap_8","uri":"capability://image.visual.data.visualization.generation.from.query.results","name":"data visualization generation from query results","description":"Automatically generates appropriate visualizations (charts, graphs, tables) based on query results and the type of data returned. The system likely analyzes result set structure (e.g., time series, categorical aggregations, distributions) and recommends or generates visualizations (line charts for trends, bar charts for comparisons, etc.). This bridges the gap between query results and actionable insights without requiring manual chart configuration.","intents":["I want to visualize query results without manually creating charts","I want the system to recommend the best chart type for my data","I want to export results as visualizations for presentations"],"best_for":["business users who need quick visualizations for presentations","analysts exploring data visually","teams building data-driven dashboards"],"limitations":["Visualization recommendations are heuristic-based and may not be optimal for domain-specific use cases","No interactive drill-down or filtering within visualizations","Large result sets (10,000+ rows) may render slowly or produce cluttered charts","Limited customization — users cannot easily modify colors, labels, or chart types without manual intervention"],"requires":["Query results in structured format","Visualization library (e.g., Plotly, D3.js, Vega)","Client-side rendering capability (web browser or desktop app)"],"input_types":["structured query results (rows and columns)"],"output_types":["interactive chart (HTML/JavaScript)","static image (PNG/SVG)","visualization metadata (chart type, axes, legend)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Active database connection (PostgreSQL, MySQL, or Snowflake)","API key or authentication credentials for the target database","LLM API access (likely OpenAI or similar, depending on DataLang's backend)","Database schema must be discoverable and readable by the translation layer","Database user account with SELECT permissions on information_schema or equivalent","Network connectivity to target database","PostgreSQL 9.6+, MySQL 5.7+, or Snowflake account","Sufficient database permissions to read system catalogs","Active database connection with valid credentials","Network connectivity to database (no local caching of data)"],"failure_modes":["LLM-generated queries frequently fail on complex multi-table joins with conditional logic, requiring manual SQL correction","Accuracy degrades significantly with domain-specific terminology and industry jargon not present in training data","No explicit reasoning or query plan explanation — users cannot understand why a query was generated a certain way","Context window limitations mean very large schemas or complex business logic may be truncated or misunderstood","Schema discovery may timeout or fail on very large databases (1000+ tables) due to introspection overhead","Relationship inference is limited — foreign keys must be explicitly defined; implicit relationships are not detected","No support for dynamic schema changes mid-session; schema is cached at connection time","Sensitive column metadata (e.g., PII flags, encryption status) is not discovered and cannot inform query generation safety","Abstraction layer adds latency (typically 50-200ms per query) compared to direct database drivers","Result pagination or streaming may not preserve query semantics for ORDER BY or LIMIT clauses across pages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3333333333333333,"quality":0.6900000000000001,"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":"2026-05-24T12:16:30.282Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=datalang","compare_url":"https://unfragile.ai/compare?artifact=datalang"}},"signature":"J3Y/pyemidrDv78AsvHo9yuZVf03y1QahBPKEvb6UGsBERBd+SS6ThWjDenDSWsRcZzPR212f/+t2ysig9SxAw==","signedAt":"2026-06-20T22:33:37.881Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/datalang","artifact":"https://unfragile.ai/datalang","verify":"https://unfragile.ai/api/v1/verify?slug=datalang","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"}}