{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-powerdrill","slug":"powerdrill","name":"Powerdrill","type":"mcp","url":"https://github.com/powerdrillai/powerdrill-mcp","page_url":"https://unfragile.ai/powerdrill","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-powerdrill__cap_0","uri":"capability://tool.use.integration.mcp.based.dataset.query.execution","name":"mcp-based dataset query execution","description":"Executes structured queries against Powerdrill datasets through the Model Context Protocol (MCP) server interface, translating natural language or structured requests into dataset-specific query operations. The MCP server acts as a bridge between AI clients (Claude, other LLMs) and Powerdrill's data layer, handling request routing, parameter validation, and response serialization through standardized MCP tool schemas.","intents":["Query a Powerdrill dataset from an AI agent without writing SQL or custom code","Enable Claude or other MCP-compatible LLMs to directly access and analyze datasets","Integrate dataset queries into multi-step AI workflows without custom API clients"],"best_for":["AI agent developers building data-aware reasoning systems","Teams deploying Claude with real-time data access requirements","Non-technical users building AI workflows that need live dataset queries"],"limitations":["Query complexity limited by Powerdrill's underlying query engine capabilities","No built-in query optimization or cost estimation — relies on Powerdrill backend","MCP protocol overhead adds latency compared to direct HTTP API calls","Requires Powerdrill account and dataset configuration before MCP server can function"],"requires":["Powerdrill account with at least one configured dataset","MCP-compatible client (Claude, Cline, or other MCP consumer)","Network connectivity to Powerdrill API endpoints","Valid Powerdrill API credentials (typically environment variable or config file)"],"input_types":["natural language queries (via LLM interpretation)","structured query parameters (filters, aggregations, projections)","dataset identifiers and field names"],"output_types":["structured JSON result sets","aggregated metrics and statistics","filtered/transformed dataset rows"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_1","uri":"capability://tool.use.integration.dataset.schema.introspection.and.tool.registration","name":"dataset schema introspection and tool registration","description":"Automatically discovers Powerdrill dataset schemas (fields, types, constraints) and registers them as callable MCP tools with proper type hints and documentation. The server introspects available datasets at startup or on-demand, generating MCP tool definitions that include field metadata, query capabilities, and parameter constraints, enabling LLMs to understand what data is queryable without hardcoded knowledge.","intents":["Automatically expose all available Powerdrill datasets as tools to an LLM without manual tool definition","Enable LLMs to discover dataset structure and available fields dynamically","Generate proper type hints and documentation for dataset queries from schema metadata"],"best_for":["Teams with multiple or frequently-changing datasets who need dynamic tool registration","Developers building generic data analysis agents that work across different dataset schemas","Organizations wanting to minimize manual MCP tool configuration overhead"],"limitations":["Schema introspection latency depends on Powerdrill API response time and dataset count","Complex nested schemas or custom field types may not map cleanly to MCP tool parameters","No caching of schema metadata — each server restart re-introspects all datasets","Field-level access control not reflected in tool registration (all registered fields are queryable)"],"requires":["Powerdrill API with schema introspection endpoint","Read permissions on target datasets","MCP server runtime with schema registration capabilities"],"input_types":["dataset identifiers","schema metadata from Powerdrill API"],"output_types":["MCP tool definitions (JSON schema format)","field metadata (names, types, descriptions)","query capability descriptors"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_2","uri":"capability://planning.reasoning.natural.language.to.dataset.query.translation","name":"natural language to dataset query translation","description":"Translates natural language requests from LLMs into executable Powerdrill queries by mapping semantic intent (e.g., 'show me sales over $1000') to dataset-specific query parameters (filters, aggregations, projections). The MCP server leverages the LLM's own reasoning to interpret natural language in context of available dataset schemas, then constructs properly-typed query objects that Powerdrill's backend can execute.","intents":["Ask questions about dataset contents in plain English without learning query syntax","Enable non-technical users to analyze data through conversational AI","Build multi-turn conversations where the LLM refines queries based on results"],"best_for":["Non-technical business users analyzing data through AI assistants","Rapid prototyping of data exploration workflows","Interactive data analysis where query refinement happens conversationally"],"limitations":["Translation accuracy depends on LLM reasoning quality and dataset schema clarity","Ambiguous natural language requests may produce incorrect query interpretations","Complex multi-step analyses may require multiple query rounds, increasing latency","No query validation or cost estimation before execution — failed queries only detected after submission"],"requires":["MCP-compatible LLM client with reasoning capabilities","Clear dataset schema with descriptive field names and documentation","Powerdrill dataset with queryable fields matching natural language intent"],"input_types":["natural language text (user questions or analysis requests)"],"output_types":["Powerdrill query objects (filters, aggregations, projections)","query execution results (JSON rows or aggregated metrics)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_3","uri":"capability://data.processing.analysis.multi.dataset.aggregation.and.join.operations","name":"multi-dataset aggregation and join operations","description":"Enables querying and combining data across multiple Powerdrill datasets through MCP tool invocations that support cross-dataset joins and aggregations. The server coordinates multiple dataset queries and performs client-side or server-side aggregation/joining based on Powerdrill's capabilities, allowing LLMs to reason about relationships between datasets without manual data pipeline construction.","intents":["Correlate data from multiple datasets (e.g., join sales and customer data)","Aggregate metrics across related datasets in a single analysis","Build composite views of data without creating new materialized datasets"],"best_for":["Teams with normalized data across multiple Powerdrill datasets","Analysts building cross-functional reports combining sales, customer, and operational data","Agents performing complex multi-source data analysis"],"limitations":["Join performance depends on dataset size and Powerdrill's join implementation","No automatic foreign key detection — join keys must be explicitly specified or inferred by LLM","Large cross-dataset queries may exceed memory or timeout limits","Aggregation logic must be expressed through Powerdrill's query API (no custom aggregation functions)"],"requires":["Multiple Powerdrill datasets with identifiable join keys","Powerdrill API support for cross-dataset queries or sufficient client-side processing capability","LLM reasoning to identify correct join relationships"],"input_types":["dataset identifiers for join sources","join key specifications (field names or expressions)","aggregation parameters (grouping, metrics)"],"output_types":["joined result sets (flattened rows with fields from multiple datasets)","aggregated metrics across datasets","correlation analysis results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_4","uri":"capability://data.processing.analysis.streaming.result.pagination.and.large.dataset.handling","name":"streaming result pagination and large dataset handling","description":"Handles pagination and streaming of large query results through MCP tool invocations, allowing LLMs to iteratively fetch dataset rows without loading entire result sets into memory. The server implements cursor-based or offset-based pagination, enabling analysis of datasets larger than typical context windows through multi-turn interactions where the LLM requests subsequent pages as needed.","intents":["Analyze large datasets that exceed LLM context window limits","Iteratively explore data by fetching results in manageable chunks","Reduce memory usage and latency by streaming results on-demand"],"best_for":["Agents analyzing datasets with millions of rows","Interactive data exploration where full result sets are unnecessary","Memory-constrained environments or rate-limited API scenarios"],"limitations":["Pagination adds latency for sequential result fetching","LLM must maintain state across multiple tool calls to track pagination position","Sorting or filtering on paginated results may require re-querying with different parameters","No built-in result caching — repeated pagination requests re-fetch from Powerdrill"],"requires":["Powerdrill API support for pagination (offset/limit or cursor-based)","MCP client capable of multi-turn tool invocations","Sufficient LLM context to track pagination state across turns"],"input_types":["query parameters (filters, sorts)","pagination parameters (page size, cursor/offset)","result format preferences"],"output_types":["paginated result sets (JSON rows)","pagination metadata (total count, next cursor, has_more flag)","streaming result chunks"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_5","uri":"capability://data.processing.analysis.query.result.caching.and.memoization","name":"query result caching and memoization","description":"Caches query results in memory or persistent storage to avoid redundant Powerdrill API calls when the same query is executed multiple times within a session or across sessions. The server implements cache key generation from query parameters, TTL-based expiration, and optional persistence to disk, enabling faster response times for repeated analyses and reducing load on the Powerdrill backend.","intents":["Speed up repeated queries by serving cached results instead of re-querying Powerdrill","Reduce API rate limit consumption for frequently-accessed datasets","Enable offline analysis of recently-queried datasets"],"best_for":["Agents performing iterative analysis with repeated queries","Teams with rate-limited Powerdrill API access","Interactive analysis sessions where query results are referenced multiple times"],"limitations":["Stale cache may serve outdated data if Powerdrill datasets are frequently updated","Cache invalidation logic must be manually configured (no automatic detection of data changes)","Memory overhead for large result sets if caching is enabled","Cache key generation may miss semantic equivalence (e.g., different filter orderings)"],"requires":["MCP server with in-memory or persistent cache storage","Cache configuration (TTL, size limits, persistence strategy)","Mechanism to invalidate cache when datasets are updated"],"input_types":["query parameters (used to generate cache keys)","cache control directives (bypass cache, force refresh)"],"output_types":["cached query results (JSON rows or metrics)","cache metadata (hit/miss status, age, TTL remaining)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_6","uri":"capability://safety.moderation.error.handling.and.query.validation.with.user.feedback","name":"error handling and query validation with user feedback","description":"Validates query parameters before execution and provides detailed error messages when queries fail, helping LLMs understand why a query was invalid and how to correct it. The server implements schema validation, type checking, and constraint verification, returning structured error responses that include the specific validation failure, affected fields, and suggested corrections.","intents":["Provide clear feedback when a query is malformed or references non-existent fields","Enable LLMs to self-correct invalid queries without trial-and-error","Prevent invalid queries from reaching the Powerdrill backend, reducing wasted API calls"],"best_for":["Agents learning to query new datasets through trial-and-error","Non-technical users who need clear guidance on query errors","Teams wanting to minimize failed API calls and improve agent efficiency"],"limitations":["Validation logic must be kept in sync with Powerdrill's actual query constraints","Complex validation rules may not be expressible in MCP tool schemas","Error messages depend on quality of schema metadata and field documentation","Some errors only detectable at query execution time (e.g., data type mismatches in aggregations)"],"requires":["Complete and accurate dataset schema metadata","Validation logic matching Powerdrill's query engine constraints","Structured error response format (JSON with error codes and messages)"],"input_types":["query parameters (filters, aggregations, projections)","dataset schema metadata"],"output_types":["validation success/failure status","detailed error messages with field names and constraint violations","suggested corrections or alternative query parameters"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_7","uri":"capability://safety.moderation.dataset.access.control.and.permission.enforcement","name":"dataset access control and permission enforcement","description":"Enforces Powerdrill dataset access controls at the MCP server level, ensuring that only authorized queries are executed based on user credentials and dataset permissions. The server validates user identity, checks dataset-level and field-level access permissions, and prevents unauthorized data access before queries reach the Powerdrill backend.","intents":["Restrict dataset access to authorized users based on Powerdrill permissions","Prevent LLMs from querying sensitive datasets without proper credentials","Enforce field-level access control (e.g., hide PII fields from certain users)"],"best_for":["Organizations with sensitive data requiring strict access control","Multi-tenant deployments where users should only access their own datasets","Compliance-heavy environments (healthcare, finance) requiring audit trails"],"limitations":["Permission enforcement depends on Powerdrill's access control API","No built-in audit logging — requires external logging infrastructure","Field-level access control may not be fully supported by Powerdrill","LLM context may inadvertently leak information about inaccessible datasets"],"requires":["Powerdrill API with user authentication and permission checking","User credentials or API keys passed to MCP server","Powerdrill dataset permissions configured for each user/role"],"input_types":["user credentials (API key, OAuth token, etc.)","dataset identifiers","query parameters"],"output_types":["authorization success/failure status","access denied errors with reason","filtered dataset/field lists (only accessible items)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-powerdrill__cap_8","uri":"capability://planning.reasoning.query.performance.monitoring.and.optimization.suggestions","name":"query performance monitoring and optimization suggestions","description":"Monitors query execution performance (latency, data scanned, rows returned) and provides optimization suggestions to LLMs when queries are slow or inefficient. The server tracks metrics like query duration, result set size, and filter selectivity, then recommends optimizations such as adding filters, using aggregations instead of raw data, or breaking large queries into smaller chunks.","intents":["Identify slow or inefficient queries and suggest optimizations","Help LLMs learn efficient query patterns for large datasets","Reduce unnecessary data transfer and processing by recommending aggregations"],"best_for":["Agents analyzing large datasets where query efficiency matters","Teams optimizing API costs by reducing data transfer","Interactive analysis where slow queries degrade user experience"],"limitations":["Performance metrics depend on Powerdrill backend reporting (may not be available)","Optimization suggestions are heuristic-based and may not apply to all query patterns","No query plan analysis or index utilization visibility","Suggestions require LLM to understand and act on optimization advice"],"requires":["Powerdrill API with query performance metrics (duration, rows scanned, etc.)","Baseline performance data to identify slow queries","Optimization rules/heuristics configured in MCP server"],"input_types":["query parameters","query execution results and metrics"],"output_types":["performance metrics (duration, rows scanned, result size)","optimization suggestions (add filters, use aggregations, etc.)","estimated performance improvements"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Powerdrill account with at least one configured dataset","MCP-compatible client (Claude, Cline, or other MCP consumer)","Network connectivity to Powerdrill API endpoints","Valid Powerdrill API credentials (typically environment variable or config file)","Powerdrill API with schema introspection endpoint","Read permissions on target datasets","MCP server runtime with schema registration capabilities","MCP-compatible LLM client with reasoning capabilities","Clear dataset schema with descriptive field names and documentation","Powerdrill dataset with queryable fields matching natural language intent"],"failure_modes":["Query complexity limited by Powerdrill's underlying query engine capabilities","No built-in query optimization or cost estimation — relies on Powerdrill backend","MCP protocol overhead adds latency compared to direct HTTP API calls","Requires Powerdrill account and dataset configuration before MCP server can function","Schema introspection latency depends on Powerdrill API response time and dataset count","Complex nested schemas or custom field types may not map cleanly to MCP tool parameters","No caching of schema metadata — each server restart re-introspects all datasets","Field-level access control not reflected in tool registration (all registered fields are queryable)","Translation accuracy depends on LLM reasoning quality and dataset schema clarity","Ambiguous natural language requests may produce incorrect query interpretations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.047Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=powerdrill","compare_url":"https://unfragile.ai/compare?artifact=powerdrill"}},"signature":"U7fspISWpeH8R6lijwEuBAyWedrVepF9aKro6AElebRi9nOZtabeX9uSI5bP/54zc40kAWoZQNtvtvh4z6epCg==","signedAt":"2026-06-22T19:01:02.844Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/powerdrill","artifact":"https://unfragile.ai/powerdrill","verify":"https://unfragile.ai/api/v1/verify?slug=powerdrill","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"}}