{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-text-to-graphql","slug":"text-to-graphql","name":"Text-To-GraphQL","type":"mcp","url":"https://github.com/Arize-ai/text-to-graphql-mcp","page_url":"https://unfragile.ai/text-to-graphql","categories":["mcp-servers","app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-text-to-graphql__cap_0","uri":"capability://text.generation.language.natural.language.to.graphql.query.translation","name":"natural-language-to-graphql-query-translation","description":"Converts natural language descriptions into valid GraphQL queries using a LangGraph-based agent that orchestrates multi-step workflows including intent recognition, schema analysis, query construction, and validation. The agent maintains state across steps and uses OpenAI's GPT-4o model to understand user intent and map it to GraphQL operations, handling complex nested queries and field selection automatically.","intents":["I want to ask for data in plain English and have it automatically converted to a GraphQL query","I need to query a GraphQL API without learning GraphQL syntax","I want Claude or Cursor to generate GraphQL queries from my natural language descriptions"],"best_for":["AI assistant integrations (Claude Desktop, Cursor) querying GraphQL APIs","developers building natural language interfaces to GraphQL backends","teams wanting to abstract GraphQL complexity from non-technical users"],"limitations":["Requires valid GraphQL schema introspection endpoint to understand available types and fields","Agent workflow adds latency per query (multiple LLM calls for intent recognition, construction, validation)","Complex mutations with nested input types may require clarification or fail validation","No built-in caching of schema introspection results — re-fetches on each MCP server restart"],"requires":["Python 3.9+","OpenAI API key with GPT-4o access","GraphQL endpoint with introspection enabled","MCP client (Claude Desktop or Cursor with MCP support)"],"input_types":["natural language text query","GraphQL endpoint URL","optional query parameters or filters"],"output_types":["valid GraphQL query string","query execution results (JSON)","validation errors with remediation suggestions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_1","uri":"capability://data.processing.analysis.graphql.schema.introspection.and.caching","name":"graphql-schema-introspection-and-caching","description":"Fetches and parses GraphQL schema via introspection queries, extracting type definitions, fields, arguments, and relationships. The system caches schema metadata in memory during the agent session and uses it to validate query construction, providing the agent with a ground-truth representation of available operations without requiring manual schema definition.","intents":["I want the system to automatically understand what fields and types are available in my GraphQL API","I need schema validation to prevent queries against non-existent fields","I want to avoid manually defining or uploading GraphQL schema files"],"best_for":["dynamic GraphQL APIs where schema changes frequently","teams using introspection-enabled GraphQL endpoints","scenarios where schema is the source of truth for query validation"],"limitations":["Introspection queries add initial latency (typically 500ms-2s depending on schema size)","Schema cache is in-memory only — not persisted across MCP server restarts","Large schemas (>10,000 types) may cause memory overhead and slower agent processing","Requires GraphQL introspection to be enabled on the endpoint (security consideration)"],"requires":["GraphQL endpoint with introspection query support enabled","Network access to the GraphQL endpoint","HTTPX HTTP client (included in dependencies)"],"input_types":["GraphQL endpoint URL","optional authentication headers"],"output_types":["parsed schema object with type definitions","field metadata (arguments, return types, nullability)","validation rules derived from schema"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_10","uri":"capability://safety.moderation.structured.exception.hierarchy.and.error.handling","name":"structured-exception-hierarchy-and-error-handling","description":"Implements a structured exception hierarchy for different error types (schema errors, query construction errors, validation errors, execution errors), enabling fine-grained error handling and recovery. Each exception type carries context information (error message, affected query, suggestions) that helps the agent or user understand what went wrong and how to fix it.","intents":["I want to distinguish between different types of errors (schema vs query vs execution)","I need detailed error context to understand what went wrong","I want the system to suggest how to fix errors"],"best_for":["agent workflows requiring error recovery based on error type","user-facing systems where detailed error messages improve experience","debugging scenarios where error context helps identify root causes"],"limitations":["Exception hierarchy adds complexity — requires understanding different exception types","Error recovery is heuristic-based and may not work for all error types","Exception messages may expose sensitive information (endpoint URLs, query details) — requires careful handling","No built-in error telemetry or aggregation — errors must be logged manually"],"requires":["Python 3.9+","custom exception classes (defined in exceptions.py)"],"input_types":["error conditions from agent steps and tools"],"output_types":["structured exception object with error type, message, and context","error recovery suggestions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_11","uri":"capability://planning.reasoning.option.selection.and.disambiguation.tools","name":"option-selection-and-disambiguation-tools","description":"Provides tools for handling ambiguous queries where multiple valid interpretations exist, presenting options to the user or agent and enabling selection of the intended interpretation. When a natural language query could map to multiple GraphQL operations or field selections, the system generates options and waits for disambiguation before proceeding.","intents":["I want the system to ask for clarification when my query is ambiguous","I need to choose between multiple valid interpretations of my request","I want to avoid the system making incorrect assumptions about my intent"],"best_for":["conversational interfaces where clarification improves accuracy","scenarios with ambiguous natural language that could have multiple interpretations","user-facing systems where wrong assumptions are costly"],"limitations":["Option presentation adds latency and requires user interaction — not suitable for fully automated workflows","No built-in option ranking — all options are presented equally even if some are more likely","Limited to GraphQL-level disambiguation — cannot resolve business logic ambiguities","Requires user or agent to select from options — no automatic selection based on context"],"requires":["ambiguous query detection logic","option generation based on schema and intent","user or agent interaction for option selection"],"input_types":["ambiguous natural language query","multiple possible interpretations"],"output_types":["list of options with descriptions","selected option from user or agent"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_12","uri":"capability://text.generation.language.data.visualization.and.result.formatting","name":"data-visualization-and-result-formatting","description":"Formats GraphQL query results for presentation to users, supporting multiple output formats (JSON, table, tree view) and handling large result sets gracefully. The system can truncate large results, highlight important fields, and provide summary statistics, making results more readable and actionable in AI assistant interfaces.","intents":["I want to see GraphQL results in a readable format","I need to handle large result sets without overwhelming the user","I want to highlight important fields or summary statistics"],"best_for":["user-facing AI assistant interfaces where result readability matters","scenarios with large result sets that need summarization","teams wanting to customize result presentation"],"limitations":["Formatting adds ~50-100ms overhead per result","Large result sets (>10MB) may be truncated or summarized, losing detail","No built-in visualization beyond text formatting — charts or graphs require external tools","Formatting is opinionated — may not match user preferences for all result types"],"requires":["GraphQL execution result (JSON)","optional formatting configuration"],"input_types":["GraphQL result object (JSON)"],"output_types":["formatted result string (JSON, table, tree view)","summary statistics","truncation indicators"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_2","uri":"capability://planning.reasoning.intent.recognition.and.query.planning","name":"intent-recognition-and-query-planning","description":"Analyzes natural language input to identify user intent (fetch, filter, aggregate, mutate) and maps it to GraphQL operations. Uses LLM-based reasoning to decompose complex requests into query components (root type, fields, filters, sorting, pagination) and generates a query plan before constructing the actual GraphQL syntax, enabling the agent to handle ambiguous or multi-step requests.","intents":["I want the system to understand what I'm asking for (data retrieval vs mutation vs aggregation)","I need to express complex queries with filters, sorting, and pagination in natural language","I want the system to ask clarifying questions if my request is ambiguous"],"best_for":["conversational AI interfaces where intent must be inferred from natural language","complex query scenarios requiring multi-field selection and filtering","use cases where query planning improves accuracy over direct generation"],"limitations":["Intent recognition depends on LLM quality — may misinterpret ambiguous or domain-specific language","No built-in clarification dialog — agent may proceed with incorrect intent if confidence is high","Requires additional LLM calls for planning, adding ~500ms-1s latency per query","Limited to intents supported by GraphQL (no custom business logic inference)"],"requires":["OpenAI API key with GPT-4o access","Natural language input with sufficient context","GraphQL schema for intent validation"],"input_types":["natural language text describing desired operation"],"output_types":["structured intent object (operation type, target type, filters, fields)","query plan (step-by-step breakdown)","confidence score and alternative interpretations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_3","uri":"capability://code.generation.editing.graphql.query.construction.with.field.selection","name":"graphql-query-construction-with-field-selection","description":"Builds valid GraphQL query syntax from intent and schema metadata, automatically selecting appropriate fields, constructing nested selections, and handling arguments. The system uses schema-aware field selection to include only requested fields and their required sub-fields, generating syntactically valid GraphQL that matches the schema structure without manual field enumeration.","intents":["I want the system to automatically select the right fields from the schema","I need to construct nested GraphQL queries without writing the syntax myself","I want to avoid manually specifying which sub-fields to include in nested types"],"best_for":["scenarios with deeply nested GraphQL types requiring complex field selection","use cases where field selection should be automatic based on intent","teams building query builders that abstract GraphQL syntax"],"limitations":["Field selection is greedy by default — may include unnecessary fields, increasing query size and latency","No built-in field optimization or cost analysis — doesn't minimize query complexity","Circular references in schema may cause infinite recursion if not handled (requires depth limits)","Custom scalar types and directives may not be handled correctly without explicit configuration"],"requires":["valid GraphQL schema with type definitions","intent object specifying desired fields and operations","graphql-core library for query validation"],"input_types":["intent object with operation type and field selections","GraphQL schema metadata"],"output_types":["valid GraphQL query string","field selection tree (for debugging)","query complexity metrics"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_4","uri":"capability://safety.moderation.graphql.query.validation.and.error.recovery","name":"graphql-query-validation-and-error-recovery","description":"Validates constructed GraphQL queries against the schema using graphql-core validation rules before execution, catching syntax errors, type mismatches, and invalid field selections. If validation fails, the agent analyzes the error and attempts recovery by reconstructing the query with corrections, providing detailed error messages to guide the user or the agent toward valid queries.","intents":["I want to catch query errors before sending them to the GraphQL endpoint","I need detailed error messages explaining what's wrong with my query","I want the system to automatically fix common query construction errors"],"best_for":["production systems where invalid queries should be prevented before execution","debugging scenarios where detailed validation errors help understand schema mismatches","agent workflows where error recovery improves success rates"],"limitations":["Validation only checks schema compliance — doesn't catch semantic errors (e.g., requesting data the user doesn't have permission to access)","Error recovery is heuristic-based and may not fix complex validation failures","No built-in performance validation — doesn't warn about expensive queries or N+1 patterns","Custom validation rules (e.g., business logic constraints) require manual implementation"],"requires":["graphql-core library with validation rules","valid GraphQL schema","constructed GraphQL query string"],"input_types":["GraphQL query string","GraphQL schema"],"output_types":["validation result (pass/fail)","detailed error list with locations and suggestions","corrected query (if recovery succeeds)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_5","uri":"capability://tool.use.integration.graphql.query.execution.with.result.handling","name":"graphql-query-execution-with-result-handling","description":"Executes validated GraphQL queries against the target endpoint using HTTPX HTTP client, handling authentication headers, timeouts, and response parsing. The system returns structured JSON results and handles GraphQL-specific error responses (including partial results with errors), providing clear error messages when execution fails and supporting both query and mutation operations.","intents":["I want to execute the generated GraphQL query against the actual endpoint","I need to handle authentication and custom headers for the GraphQL request","I want to see the results in a structured format with error handling"],"best_for":["end-to-end query execution in MCP clients (Claude Desktop, Cursor)","scenarios where results must be returned to the user immediately","systems requiring authentication or custom headers for GraphQL endpoints"],"limitations":["No built-in retry logic or circuit breaker — single attempt per execution","Timeout is fixed (typically 30s) — not configurable per query","No query result caching — identical queries executed multiple times hit the endpoint each time","Large result sets (>10MB) may cause memory issues or timeout","No built-in pagination handling — user must request paginated results explicitly"],"requires":["HTTPX HTTP client library","valid GraphQL endpoint URL","optional authentication headers or API keys","network access to the GraphQL endpoint"],"input_types":["valid GraphQL query string","GraphQL endpoint URL","optional authentication headers"],"output_types":["JSON result object","error response with GraphQL error details","execution metadata (latency, result size)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_6","uri":"capability://tool.use.integration.mcp.protocol.server.with.tool.registration","name":"mcp-protocol-server-with-tool-registration","description":"Implements a FastMCP-based Model Context Protocol server that exposes text-to-graphql capabilities as MCP tools, enabling seamless integration with MCP clients like Claude Desktop and Cursor. The server registers tools for schema management, query construction, validation, and execution, handling MCP protocol serialization and client communication automatically.","intents":["I want to integrate text-to-graphql capabilities into Claude Desktop or Cursor","I need to expose GraphQL query generation as an MCP tool for AI assistants","I want to enable natural language GraphQL querying in my AI-powered IDE or chat interface"],"best_for":["Claude Desktop and Cursor integrations requiring GraphQL query capabilities","teams building MCP servers for AI assistant ecosystems","developers wanting to expose GraphQL APIs to AI assistants without custom integration code"],"limitations":["MCP protocol overhead adds ~100-200ms per tool invocation","Tool output is limited to text/JSON serialization — no streaming results","No built-in authentication for MCP clients — relies on client-side credential management","Server must be running separately from the MCP client — requires process management"],"requires":["FastMCP framework (included in dependencies)","Python 3.9+","MCP client (Claude Desktop 0.4+ or Cursor with MCP support)","stdio or HTTP transport configuration for MCP communication"],"input_types":["MCP tool invocation with parameters (natural language query, endpoint URL, etc.)"],"output_types":["MCP tool result (JSON-serialized GraphQL query or execution result)","MCP error response with error details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_7","uri":"capability://planning.reasoning.langgraph.agent.state.orchestration","name":"langgraph-agent-state-orchestration","description":"Orchestrates the multi-step text-to-graphql workflow using LangGraph state machine, maintaining agent state across steps (intent, schema, query plan, constructed query, validation result, execution result). The agent transitions between states based on step outcomes, enabling error recovery, iterative refinement, and clear visibility into the query generation process.","intents":["I want to understand the step-by-step process of converting natural language to GraphQL","I need error recovery and iterative refinement if a step fails","I want to debug the query generation process by inspecting intermediate states"],"best_for":["complex query generation workflows requiring multi-step orchestration","scenarios where error recovery and iterative refinement improve success rates","debugging and observability of AI agent behavior"],"limitations":["LangGraph state management adds ~50-100ms overhead per state transition","State size grows with query complexity — large schemas or long conversation histories may cause memory issues","No built-in state persistence — agent state is lost if the MCP server restarts","Debugging requires understanding LangGraph state machine semantics — steeper learning curve than sequential code"],"requires":["LangGraph library (included in dependencies)","LangChain for LLM integration","Python 3.9+"],"input_types":["initial state (natural language query, endpoint URL)","step outputs (intent, schema, query plan, etc.)"],"output_types":["final agent state with all intermediate results","execution trace showing state transitions","error state with recovery suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_8","uri":"capability://automation.workflow.structured.logging.and.observability","name":"structured-logging-and-observability","description":"Provides structured logging using Loguru with MCP mode detection, logging all agent steps, tool invocations, schema operations, and query execution with timestamps and context. The system detects whether it's running in MCP mode and adjusts logging output accordingly, enabling debugging and observability without interfering with MCP protocol communication.","intents":["I want to debug the query generation process by inspecting logs","I need to monitor agent performance and identify bottlenecks","I want to understand what's happening at each step of the workflow"],"best_for":["production deployments requiring observability and debugging","development scenarios where detailed logs help understand agent behavior","teams monitoring MCP server health and performance"],"limitations":["Structured logging adds ~10-20ms overhead per log entry","Logs are written to stdout/stderr — no built-in log aggregation or persistence","Log verbosity may expose sensitive information (API keys, query details) — requires careful configuration","No built-in log rotation or size management — logs may grow unbounded"],"requires":["Loguru library (included in dependencies)","Python 3.9+","stdout/stderr access for log output"],"input_types":["log events from agent steps and tools"],"output_types":["structured log entries with timestamps, context, and severity","human-readable log output"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-text-to-graphql__cap_9","uri":"capability://automation.workflow.pydantic.configuration.management","name":"pydantic-configuration-management","description":"Manages system configuration using Pydantic settings, supporting environment variables, configuration files, and runtime overrides. The system validates configuration at startup and provides sensible defaults for GraphQL endpoint, API keys, logging levels, and agent parameters, enabling flexible deployment across different environments.","intents":["I want to configure the MCP server for different environments (dev, staging, production)","I need to manage API keys and endpoint URLs without hardcoding them","I want to validate configuration at startup to catch errors early"],"best_for":["production deployments requiring environment-specific configuration","teams managing multiple MCP server instances with different settings","scenarios where configuration must be validated before startup"],"limitations":["Pydantic validation adds ~10-50ms startup overhead","Configuration is loaded once at startup — runtime changes require server restart","No built-in configuration hot-reload — changes to environment variables don't take effect until restart","Sensitive configuration (API keys) may be logged or exposed in error messages if not carefully handled"],"requires":["Pydantic library (included in dependencies)","Python 3.9+","environment variables or configuration file"],"input_types":["environment variables","configuration file (JSON, YAML, or .env)","runtime configuration overrides"],"output_types":["validated configuration object","configuration validation errors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","OpenAI API key with GPT-4o access","GraphQL endpoint with introspection enabled","MCP client (Claude Desktop or Cursor with MCP support)","GraphQL endpoint with introspection query support enabled","Network access to the GraphQL endpoint","HTTPX HTTP client (included in dependencies)","custom exception classes (defined in exceptions.py)","ambiguous query detection logic","option generation based on schema and intent"],"failure_modes":["Requires valid GraphQL schema introspection endpoint to understand available types and fields","Agent workflow adds latency per query (multiple LLM calls for intent recognition, construction, validation)","Complex mutations with nested input types may require clarification or fail validation","No built-in caching of schema introspection results — re-fetches on each MCP server restart","Introspection queries add initial latency (typically 500ms-2s depending on schema size)","Schema cache is in-memory only — not persisted across MCP server restarts","Large schemas (>10,000 types) may cause memory overhead and slower agent processing","Requires GraphQL introspection to be enabled on the endpoint (security consideration)","Exception hierarchy adds complexity — requires understanding different exception types","Error recovery is heuristic-based and may not work for all error types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:04.050Z","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=text-to-graphql","compare_url":"https://unfragile.ai/compare?artifact=text-to-graphql"}},"signature":"qhhR/GaNAhs1GkAmfaQ3EKYroht0MzYpdqatKIIcLHWVpDi7LWeFLyvZLA/KDxBrDYsUy3qostRqsosAVFLICg==","signedAt":"2026-06-21T15:43:04.100Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/text-to-graphql","artifact":"https://unfragile.ai/text-to-graphql","verify":"https://unfragile.ai/api/v1/verify?slug=text-to-graphql","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"}}