{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-teradata","slug":"teradata","name":"Teradata","type":"mcp","url":"https://github.com/Teradata/teradata-mcp-server","page_url":"https://unfragile.ai/teradata","categories":["mcp-servers","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-teradata__cap_0","uri":"capability://tool.use.integration.mcp.protocol.bridging.with.multi.transport.support","name":"mcp protocol bridging with multi-transport support","description":"Implements the Model Context Protocol as a production-grade server that translates between AI client requests and Teradata database operations, supporting three transport mechanisms (stdio for desktop clients, streamable-http for web applications, and SSE for real-time streaming). The server acts as a protocol adapter layer that normalizes client requests into structured tool invocations while maintaining stateless request-response semantics required by MCP specification.","intents":["Connect Claude Desktop or VS Code Copilot to a Teradata database without custom integration code","Build web applications that expose Teradata functionality through a standardized protocol","Deploy a single MCP server instance that multiple AI clients can connect to simultaneously","Integrate Teradata with custom AI agents using the Model Context Protocol standard"],"best_for":["Enterprise teams standardizing on MCP for AI-database integration","Developers building multi-client AI applications targeting Teradata","Organizations migrating from REST APIs to protocol-based AI integration"],"limitations":["Stateless design means no built-in session persistence across requests — requires external state management for multi-step workflows","Transport selection is fixed at server startup — cannot dynamically switch between stdio, HTTP, and SSE without restart","MCP protocol overhead adds ~50-100ms per request due to JSON serialization and protocol handshaking"],"requires":["Python 3.9+","Teradata database instance with network accessibility","MCP-compatible client (Claude Desktop, VS Code with Copilot, or custom implementation)","Model Context Protocol specification v1.0 or later understanding"],"input_types":["JSON-RPC 2.0 requests following MCP specification","Tool invocation payloads with typed parameters","Configuration objects for transport and security settings"],"output_types":["JSON-RPC 2.0 responses with tool results","Structured error objects with diagnostic information","Streaming responses for long-running operations (SSE transport)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_1","uri":"capability://tool.use.integration.dynamic.tool.loading.and.registration.with.module.introspection","name":"dynamic tool loading and registration with module introspection","description":"Implements a plugin architecture using Python's module introspection (via module_loader.py) that dynamically discovers, loads, and registers tools from the tools directory at server startup. Tools are organized into categories (Base, DBA, Data Quality, Security, Analytics, RAG, Chat Completion, SQL Optimization, Feature Store) and registered with the MCP server's tool registry, enabling extensibility without modifying core server code. Each tool is introspected for its schema, input parameters, and docstrings to auto-generate MCP tool definitions.","intents":["Add new database management tools without restarting the server or modifying server.py","Organize tools by functional domain (DBA, data quality, security) for better discoverability","Automatically expose tool schemas to clients based on Python function signatures and type hints","Create custom tools by dropping Python files into the tools directory"],"best_for":["Teams building extensible database management platforms","Organizations with custom tool requirements that evolve frequently","Developers who want to add tools without understanding MCP protocol details"],"limitations":["Module loader requires tools to follow specific naming conventions and function signatures — non-compliant tools are silently skipped","Dynamic loading happens only at server startup — adding tools requires server restart","No built-in tool versioning or dependency management between tools","Tool discovery is filesystem-based, making it difficult to load tools from external packages or remote sources"],"requires":["Python 3.9+","Tools must be Python modules in src/teradata_mcp_server/tools/ directory","Tools must expose functions with type hints for parameter schema generation","Understanding of MCP tool definition format (name, description, inputSchema)"],"input_types":["Python module files (.py) in tools directory","Function signatures with type annotations","Docstrings for tool descriptions"],"output_types":["Registered MCP tool definitions with JSON schemas","Tool metadata (name, description, input parameters)","Callable tool functions mapped to MCP tool names"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_10","uri":"capability://automation.workflow.configuration.management.with.environment.variables.profiles.and.yaml.files","name":"configuration management with environment variables, profiles, and yaml files","description":"Implements a flexible configuration system that supports multiple configuration sources (environment variables, YAML files, configuration profiles) with a hierarchical precedence model. Configuration covers database connectivity, tool behavior, security policies, RAG settings, chat completion rules, SQL optimization strategies, and feature store definitions. The configuration system allows different deployment environments (development, staging, production) to use different configurations without code changes, and supports profile-based configuration selection for multi-tenant deployments.","intents":["Configure the MCP server for different deployment environments without code changes","Manage multiple database profiles for multi-tenant or multi-database deployments","Define tool behavior, security policies, and optimization rules through configuration","Support secrets management through environment variables for secure credential handling"],"best_for":["DevOps teams managing MCP server deployments across multiple environments","Organizations with complex configuration requirements and multiple deployment targets","Teams needing to support multi-tenant deployments with isolated configurations"],"limitations":["Configuration is loaded at server startup — changes require server restart to take effect","YAML configuration files are not validated at load time — invalid configurations may cause runtime errors","Environment variable precedence can be confusing when multiple configuration sources are present","No built-in configuration versioning or rollback — requires external version control for configuration management"],"requires":["YAML configuration files for complex settings (optional, defaults are used if not provided)","Environment variables for sensitive data (API keys, database credentials)","Understanding of configuration file format and precedence rules"],"input_types":["Environment variables (TERADATA_HOST, TERADATA_USER, etc.)","YAML configuration files (config.yaml, profiles.yaml, rag.yaml, etc.)","Profile names for selecting configuration variants","Feature flags for enabling/disabling functionality"],"output_types":["Resolved configuration values","Configuration validation results","Active configuration profile information","Configuration source information (which file/variable provided each setting)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_11","uri":"capability://tool.use.integration.client.integration.with.claude.desktop.vs.code.open.webui.and.flowise","name":"client integration with claude desktop, vs code, open webui, and flowise","description":"Provides pre-built integration configurations and quick-start guides for connecting the Teradata MCP server to popular AI client applications including Claude Desktop, VS Code with Copilot, Open WebUI, and Flowise. Integration involves configuring the client to connect to the MCP server via the appropriate transport mechanism (stdio for desktop clients, HTTP for web applications), and registering the server's tools with the client. Each integration includes step-by-step setup instructions, configuration examples, and troubleshooting guides.","intents":["Connect Claude Desktop to Teradata database without manual MCP configuration","Use VS Code Copilot to query Teradata data and manage databases","Build web-based AI applications using Open WebUI or Flowise with Teradata backend","Integrate Teradata with existing AI agent frameworks and applications"],"best_for":["Users new to MCP wanting to quickly connect existing AI clients to Teradata","Teams building AI applications using popular frameworks (Flowise, Open WebUI)","Organizations standardizing on specific AI client platforms"],"limitations":["Integration guides are specific to supported clients — other clients require custom integration work","Client configuration is manual — no automated client discovery or registration","Transport mechanism must match client capabilities — some clients only support specific transports","Integration examples assume default server configuration — custom configurations may require manual adjustment"],"requires":["Teradata MCP server running and accessible to the client","Supported client application installed (Claude Desktop, VS Code, Open WebUI, Flowise)","Network connectivity between client and server","Client-specific configuration (API keys, server URL, etc.)"],"input_types":["Client configuration files (JSON, YAML)","Server connection parameters (host, port, transport)","Client-specific settings (API keys, model selection)"],"output_types":["Registered MCP tools in client interface","Tool descriptions and parameter schemas","Tool execution results in client format","Error messages and diagnostics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_12","uri":"capability://automation.workflow.deployment.and.scaling.with.docker.systemd.and.monitoring","name":"deployment and scaling with docker, systemd, and monitoring","description":"Provides deployment patterns and configurations for running the Teradata MCP server in production environments, including Docker containerization, systemd service management, monitoring and logging integration, and high-availability setup. Deployment documentation covers container image building, environment variable configuration, log aggregation, health checks, and scaling strategies for multi-instance deployments. Monitoring integration enables tracking server health, tool execution metrics, and database connection statistics.","intents":["Deploy Teradata MCP server as a containerized service in Kubernetes or Docker Compose","Run MCP server as a systemd service on Linux with automatic restart and logging","Monitor server health, tool execution performance, and database connectivity","Scale MCP server horizontally across multiple instances with load balancing"],"best_for":["DevOps teams deploying MCP server in production environments","Organizations requiring high-availability and scalable database access","Teams implementing observability and monitoring for AI infrastructure"],"limitations":["Horizontal scaling requires external load balancing and session management — in-process state is not shared across instances","Docker deployment requires container registry and orchestration platform — adds operational complexity","Monitoring integration is optional — requires external monitoring infrastructure (Prometheus, ELK, etc.)","Health checks are basic (server responsiveness) — do not validate database connectivity or tool functionality"],"requires":["Docker or container runtime for containerized deployment","systemd or similar service manager for service-based deployment","Monitoring infrastructure (Prometheus, ELK, CloudWatch) for observability","Load balancer for horizontal scaling (nginx, HAProxy, cloud load balancer)"],"input_types":["Docker image configuration (Dockerfile)","systemd service file configuration","Monitoring and logging configuration","Environment variables for deployment-specific settings"],"output_types":["Running MCP server instance","Health check results","Metrics and logs for monitoring","Deployment status and diagnostics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_2","uri":"capability://data.processing.analysis.teradata.database.connectivity.with.connection.pooling.and.profile.based.access.control","name":"teradata database connectivity with connection pooling and profile-based access control","description":"Manages connections to Teradata databases using a connection pooling mechanism that reuses database connections across multiple tool invocations, reducing connection overhead. Implements profile-based access control where different database credentials and connection parameters are stored in configuration profiles, allowing the server to enforce role-based access policies and prevent unauthorized database access. Connection parameters (host, port, username, password, database) are configured via environment variables or YAML configuration files with profile selection at runtime.","intents":["Connect to multiple Teradata database instances from a single MCP server using different profiles","Enforce role-based access control by restricting which profiles different AI clients can use","Reduce database connection overhead by reusing connections across multiple tool invocations","Securely manage database credentials without embedding them in tool code"],"best_for":["Enterprise deployments with multiple Teradata instances and strict access control requirements","Multi-tenant SaaS applications serving different customers with isolated database access","Teams needing to enforce least-privilege database access for AI agents"],"limitations":["Connection pooling is in-process only — does not support distributed connection pooling across multiple server instances","Profile selection is static per server instance — cannot dynamically switch profiles per request without additional middleware","No built-in connection timeout or idle connection cleanup — long-running servers may accumulate stale connections","Credentials stored in environment variables or YAML files are vulnerable to exposure if files are not properly secured"],"requires":["Teradata database instance with network accessibility","Teradata Python driver (teradatasql or similar) installed","Environment variables or YAML configuration file with database connection parameters","Profile name specified in server configuration or environment variable"],"input_types":["Connection profile name (string)","Database credentials (username, password, host, port)","SQL queries or database operations from tools"],"output_types":["Database connection objects (pooled)","Query results (rows, metadata)","Connection status and health information"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_3","uri":"capability://automation.workflow.dba.tool.suite.for.database.administration.and.monitoring","name":"dba tool suite for database administration and monitoring","description":"Provides a collection of specialized tools for database administrators to perform common Teradata management tasks including user/role management, table creation and modification, index management, performance monitoring, and system health checks. Tools are implemented as Python functions that execute Teradata SQL commands and return structured results, with built-in error handling and validation. The DBA tool category includes tools for creating users, granting permissions, analyzing table statistics, monitoring query performance, and checking system resource utilization.","intents":["Automate routine DBA tasks like user creation and permission management through natural language requests","Monitor database performance and health metrics without logging into Teradata console","Analyze table statistics and query performance to identify optimization opportunities","Manage database objects (tables, indexes, views) through AI agent requests"],"best_for":["Database administrators seeking to automate routine management tasks","DevOps teams building self-service database provisioning workflows","Organizations implementing AI-driven database operations (AIOps)"],"limitations":["DBA tools require elevated database privileges (CREATE USER, ALTER TABLE, etc.) — cannot be used with restricted database profiles","Some operations are destructive (DROP TABLE, ALTER USER) and require additional confirmation or approval workflows","Tool execution is synchronous — long-running operations (ANALYZE TABLE, index creation) block the MCP request","No built-in audit logging of DBA operations — requires external logging integration for compliance"],"requires":["Teradata database user with DBA privileges","Database profile configured with elevated credentials","Understanding of Teradata SQL syntax and database administration concepts"],"input_types":["Natural language requests describing DBA operations","Structured parameters (user names, table names, permission types)","SQL commands or database object definitions"],"output_types":["Operation status (success/failure)","Affected row counts or object counts","Performance metrics and monitoring data","Error messages with diagnostic information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_4","uri":"capability://data.processing.analysis.data.quality.assessment.and.validation.tools","name":"data quality assessment and validation tools","description":"Implements a suite of tools for assessing and validating data quality in Teradata tables, including null value detection, duplicate detection, data type validation, statistical profiling, and schema validation. Tools execute SQL queries to analyze table contents and return quality metrics, anomalies, and recommendations. The data quality tool category provides both automated quality checks (run against all tables) and targeted validation (run against specific tables or columns) with configurable thresholds and rules.","intents":["Automatically assess data quality across multiple tables to identify data issues","Detect anomalies like unexpected null values, duplicates, or out-of-range values","Generate data quality reports with metrics and recommendations for remediation","Validate data against business rules or schema constraints before processing"],"best_for":["Data engineering teams implementing data quality monitoring pipelines","Analytics teams validating data before building reports or models","Organizations with data governance requirements needing automated quality checks"],"limitations":["Quality checks are read-only and do not automatically remediate issues — require separate tools or workflows for data cleaning","Statistical profiling can be slow on very large tables (billions of rows) — may timeout or consume significant resources","Quality thresholds are configurable but require domain knowledge to set appropriately — no automatic threshold learning","Tools provide point-in-time quality snapshots — no built-in historical tracking or trend analysis"],"requires":["Teradata database with read access to target tables","Table metadata (column names, data types) accessible to the database user","Configuration of quality rules and thresholds (optional, uses defaults if not specified)"],"input_types":["Table names or schema names to analyze","Column names for targeted validation","Quality rule definitions (null percentage threshold, duplicate threshold, etc.)","Statistical profiling parameters (sample size, histogram buckets)"],"output_types":["Quality metrics (null percentage, duplicate count, cardinality)","Anomaly reports with affected rows or columns","Data type and schema validation results","Quality score and recommendations for remediation"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_5","uri":"capability://memory.knowledge.rag.retrieval.augmented.generation.workflow.integration.with.semantic.search","name":"rag (retrieval-augmented generation) workflow integration with semantic search","description":"Implements a Retrieval-Augmented Generation pipeline that enables AI agents to query Teradata tables, retrieve relevant data based on semantic similarity, and augment LLM prompts with retrieved context. The RAG workflow uses vector embeddings to enable semantic search over table contents, allowing natural language queries like 'find similar customer records' to retrieve contextually relevant data without requiring exact SQL knowledge. Configuration is managed through RAG-specific YAML files that define which tables, columns, and embedding models to use.","intents":["Enable AI agents to retrieve contextually relevant data from Teradata using natural language queries","Augment LLM responses with real data from Teradata without requiring users to write SQL","Implement semantic search over unstructured or semi-structured data stored in Teradata","Build AI applications that combine LLM reasoning with database-backed facts"],"best_for":["Teams building AI-powered search or recommendation systems over Teradata data","Organizations implementing retrieval-augmented generation for customer-facing AI applications","Data teams needing to expose Teradata data to LLMs without exposing raw SQL access"],"limitations":["Embedding generation requires external embedding models (OpenAI, Anthropic, or local models) — adds latency and cost","Vector storage is not persistent by default — embeddings must be regenerated on server restart unless external vector database is configured","Semantic search accuracy depends on embedding model quality — may return irrelevant results for domain-specific data","RAG configuration is static at server startup — cannot dynamically add new tables or change embedding models without restart"],"requires":["Teradata database with tables containing data to be indexed","Embedding model API key (OpenAI, Anthropic) or local embedding model","RAG configuration YAML file specifying tables, columns, and embedding parameters","Vector database or in-memory vector storage (if using external persistence)"],"input_types":["Natural language queries for semantic search","Table and column names to index","Embedding model configuration (model name, API key, parameters)","Similarity threshold for retrieval"],"output_types":["Retrieved data rows with similarity scores","Formatted context for LLM augmentation","Embedding vectors (if requested)","Search metadata (number of results, similarity distribution)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_6","uri":"capability://data.processing.analysis.sql.optimization.and.query.analysis.tools","name":"sql optimization and query analysis tools","description":"Provides tools for analyzing and optimizing Teradata SQL queries, including query plan analysis, index recommendation, query rewriting suggestions, and performance profiling. Tools execute EXPLAIN PLAN commands, analyze query execution statistics, and suggest optimizations based on table statistics and index availability. SQL optimization configuration is managed through YAML files that define optimization rules, thresholds, and preferred optimization strategies.","intents":["Analyze slow-running queries to identify performance bottlenecks","Get recommendations for indexes or query rewrites to improve performance","Understand query execution plans without manual EXPLAIN PLAN analysis","Optimize queries before execution to reduce database load"],"best_for":["Database performance engineers optimizing Teradata workloads","Data teams building efficient ETL pipelines","Organizations implementing automated query optimization"],"limitations":["Optimization recommendations are heuristic-based and may not apply to all workloads — require domain expertise to evaluate","Index recommendations assume standard workload patterns — may not be optimal for specialized use cases","Query rewriting is conservative to avoid changing query semantics — may miss aggressive optimization opportunities","Performance analysis requires up-to-date table statistics — stale statistics lead to inaccurate recommendations"],"requires":["Teradata database with EXPLAIN PLAN support","Table statistics (COLLECT STATISTICS) up-to-date","SQL optimization configuration YAML file (optional, uses defaults if not specified)","Database user with SELECT access to system tables (DBC.Tables, DBC.Indexes)"],"input_types":["SQL queries to analyze","Query execution statistics or EXPLAIN PLAN output","Optimization preferences (prioritize speed vs. resource usage)","Constraint information (available indexes, table sizes)"],"output_types":["Query execution plan with estimated costs","Performance metrics (estimated rows, CPU time, I/O operations)","Optimization recommendations with expected improvement","Rewritten query suggestions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_7","uri":"capability://text.generation.language.chat.completion.and.conversational.query.interface","name":"chat completion and conversational query interface","description":"Implements a conversational interface that translates natural language questions into Teradata SQL queries and executes them, returning results in natural language format. The chat completion workflow uses LLM function calling to map user questions to appropriate tools and database operations, with configuration managed through chat completion YAML files that define conversation context, query templates, and result formatting rules. Supports multi-turn conversations where context from previous queries informs subsequent queries.","intents":["Ask natural language questions about Teradata data without writing SQL","Get conversational responses to data queries with explanations and context","Build chatbot interfaces for business users to query databases","Maintain conversation context across multiple queries for coherent interactions"],"best_for":["Non-technical business users querying databases through conversational interfaces","Teams building chatbot applications over Teradata data","Organizations implementing natural language interfaces to data warehouses"],"limitations":["LLM-generated SQL may be incorrect or inefficient — requires validation before execution","Conversation context is not persisted — multi-turn conversations are lost on server restart","Natural language understanding is limited by LLM capabilities — ambiguous questions may be misinterpreted","Result formatting is LLM-dependent — responses may be verbose or miss important details"],"requires":["LLM API key (OpenAI, Anthropic, or compatible provider)","Teradata database with read access to relevant tables","Chat completion configuration YAML file specifying conversation rules and query templates","Table schema information for LLM context"],"input_types":["Natural language questions or statements","Conversation history (previous questions and responses)","Database schema and table descriptions","Query constraints or filters"],"output_types":["Natural language responses with query results","Formatted data tables or summaries","Explanations of query logic and results","Suggestions for follow-up queries"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_8","uri":"capability://data.processing.analysis.feature.store.integration.for.ml.feature.management","name":"feature store integration for ml feature management","description":"Provides tools for managing and retrieving machine learning features stored in Teradata, including feature definition, feature computation, feature versioning, and feature retrieval for model training and inference. Feature store tools enable data scientists to define features declaratively, compute features from raw data, version features for reproducibility, and retrieve feature vectors for ML pipelines. Configuration is managed through feature store YAML files that define feature definitions, computation logic, and versioning strategies.","intents":["Define and manage ML features in Teradata without writing custom code","Compute features at scale for model training and inference","Version features to ensure reproducibility and track feature changes","Retrieve feature vectors for ML pipelines with consistent feature engineering"],"best_for":["Data science teams building ML pipelines on Teradata","Organizations implementing feature stores for ML model governance","Teams needing reproducible feature engineering across training and inference"],"limitations":["Feature computation is synchronous — large-scale feature computation may timeout or consume significant resources","Feature versioning is manual — requires explicit version management in configuration","Feature retrieval is point-in-time — no built-in time-travel or historical feature retrieval","Feature store configuration is static at server startup — cannot dynamically add or modify features without restart"],"requires":["Teradata database with raw data tables","Feature store configuration YAML file defining feature definitions and computation logic","Understanding of feature engineering and ML workflows","Metadata tables for feature versioning and tracking"],"input_types":["Feature definitions (name, description, computation logic)","Entity identifiers (customer ID, product ID) for feature retrieval","Feature versions or timestamps for historical retrieval","Feature computation parameters (aggregation windows, filters)"],"output_types":["Feature vectors with entity IDs","Feature metadata (name, version, computation time)","Feature statistics (mean, std dev, missing values)","Feature lineage and dependencies"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teradata__cap_9","uri":"capability://safety.moderation.security.and.access.control.enforcement.with.role.based.policies","name":"security and access control enforcement with role-based policies","description":"Implements security tools for managing database access control, including user authentication, role-based access control (RBAC), permission management, and audit logging. Security tools enforce policies defined in configuration files, preventing unauthorized access to sensitive data and operations. The security tool category includes tools for creating users, assigning roles, granting/revoking permissions, and auditing access patterns.","intents":["Enforce role-based access control to prevent unauthorized database access","Audit database access and operations for compliance and security monitoring","Manage user permissions and roles without direct database access","Implement least-privilege access policies for AI agents and applications"],"best_for":["Enterprise organizations with strict security and compliance requirements","Teams implementing zero-trust security models for database access","Organizations needing audit trails for regulatory compliance"],"limitations":["Security policies are enforced at the MCP server level — do not prevent direct database access if database credentials are compromised","Audit logging requires external logging infrastructure — server does not persist audit logs by default","Role definitions are static at server startup — cannot dynamically create new roles without restart","Permission checks add latency to every tool invocation — may impact performance for high-throughput workloads"],"requires":["Teradata database with user and role management support","Security configuration YAML file defining roles, permissions, and policies","Audit logging infrastructure (external log aggregation service)","Understanding of role-based access control concepts"],"input_types":["User identifiers and credentials","Role names and permission definitions","Access control policies (which roles can invoke which tools)","Audit event information (user, action, resource, timestamp)"],"output_types":["Access control decisions (allow/deny)","Audit log entries with user, action, and timestamp","Permission and role information","Security policy violation alerts"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Teradata database instance with network accessibility","MCP-compatible client (Claude Desktop, VS Code with Copilot, or custom implementation)","Model Context Protocol specification v1.0 or later understanding","Tools must be Python modules in src/teradata_mcp_server/tools/ directory","Tools must expose functions with type hints for parameter schema generation","Understanding of MCP tool definition format (name, description, inputSchema)","YAML configuration files for complex settings (optional, defaults are used if not provided)","Environment variables for sensitive data (API keys, database credentials)","Understanding of configuration file format and precedence rules"],"failure_modes":["Stateless design means no built-in session persistence across requests — requires external state management for multi-step workflows","Transport selection is fixed at server startup — cannot dynamically switch between stdio, HTTP, and SSE without restart","MCP protocol overhead adds ~50-100ms 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