{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"redis-mcp-server","slug":"redis-mcp-server","name":"Redis MCP Server","type":"mcp","url":"https://github.com/redis/mcp-redis","page_url":"https://unfragile.ai/redis-mcp-server","categories":["mcp-servers"],"tags":["redis","cache","database","community"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"redis-mcp-server__cap_0","uri":"capability://tool.use.integration.natural.language.to.redis.command.translation.via.mcp.tools","name":"natural language to redis command translation via mcp tools","description":"Translates conversational AI agent queries into Redis operations through a decorator-based tool registration system built on FastMCP framework. The RedisMCPServer class exposes Redis functionality as MCP tools, allowing agents to express intent in natural language (e.g., 'cache this item') which maps to specific Redis commands (e.g., string.set_string with expiration) without requiring knowledge of Redis syntax or command semantics.","intents":["Enable AI agents to interact with Redis using natural language instead of learning Redis command syntax","Abstract Redis complexity so non-Redis-expert developers can build Redis-backed AI applications","Translate high-level agent requests into optimized Redis operations automatically"],"best_for":["AI agent developers building applications with Redis backends","Teams integrating Claude or OpenAI agents with Redis data stores","Developers wanting to expose Redis to LLMs without custom command parsing"],"limitations":["Translation quality depends on LLM's understanding of Redis semantics — complex multi-step operations may require explicit tool chaining","No built-in query optimization — agent may generate inefficient Redis command sequences","Limited to operations exposed as MCP tools — custom Redis modules or Lua scripts not directly accessible"],"requires":["Python 3.9+","Redis instance (standalone or cluster)","FastMCP framework","MCP-compatible AI platform (Claude Desktop, OpenAI Agents SDK, or custom MCP client)"],"input_types":["natural language queries from AI agents","structured parameters passed through MCP tool invocations"],"output_types":["Redis operation results (strings, hashes, lists, sets, streams)","structured JSON responses from MCP tools"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_1","uri":"capability://tool.use.integration.multi.transport.mcp.server.deployment.stdio.sse.docker","name":"multi-transport mcp server deployment (stdio, sse, docker)","description":"Exposes Redis operations through multiple communication transports configured via MCP_TRANSPORT environment variable: stdio for local development and direct process communication, Server-Sent Events (SSE) for network-based deployments, and containerized Docker deployment with environment-based configuration. The server abstracts transport selection, allowing the same RedisMCPServer implementation to serve different deployment topologies without code changes.","intents":["Deploy Redis MCP server locally for development with Claude Desktop or VS Code","Run Redis MCP as a network service accessible to remote AI agents via HTTP/SSE","Containerize Redis MCP for Kubernetes or cloud deployments with environment-based configuration","Support multiple client types (OpenAI Agents SDK, Claude Desktop, custom MCP clients) through unified transport abstraction"],"best_for":["Development teams using Claude Desktop or VS Code with local Redis instances","Organizations deploying AI agents in cloud environments requiring network-accessible Redis","DevOps teams containerizing AI agent infrastructure with Docker/Kubernetes"],"limitations":["SSE transport is unidirectional streaming — bidirectional operations require request-response polling","Docker deployment requires managing Redis connection credentials via environment variables — no secrets management built-in","Stdio transport limited to single client per process — requires process spawning for multiple concurrent agents"],"requires":["Python 3.9+","FastMCP framework","For SSE: HTTP server infrastructure (reverse proxy, load balancer)","For Docker: Docker runtime and Redis network accessibility","MCP_TRANSPORT environment variable set to 'stdio', 'sse', or container-specific value"],"input_types":["MCP protocol messages (JSON-RPC over stdio or HTTP)","environment variables for configuration"],"output_types":["MCP protocol responses (JSON-RPC)","Server-Sent Events stream (for SSE transport)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_10","uri":"capability://search.retrieval.vector.similarity.search.with.index.creation.and.retrieval","name":"vector similarity search with index creation and retrieval","description":"Provides vector search capabilities through a redis_query_engine tool that creates vector indexes, stores embeddings, and performs similarity searches. The engine abstracts Redis Search module operations (HNSW algorithm for approximate nearest neighbor search), enabling agents to index documents with embeddings and retrieve semantically similar results. Agents can express intent like 'index and search this vector' which maps to create_index() + vector_search(), enabling semantic search without understanding vector database internals or similarity metrics.","intents":["Implement semantic search over documents or conversation history","Find similar items based on embeddings (products, articles, users)","Build recommendation systems using vector similarity","Enable natural language search over indexed content"],"best_for":["Semantic search applications with embedding-based retrieval","Recommendation systems using vector similarity","RAG (Retrieval-Augmented Generation) systems with vector indexes"],"limitations":["Requires Redis Search module (not available in Redis Community Edition) — only available in Redis Enterprise or Redis Stack","Vector dimension and similarity metric must be specified at index creation — cannot change without recreating index","Approximate nearest neighbor search may miss exact matches — trade-off between speed and accuracy","Index creation is blocking operation — large datasets may cause latency spikes"],"requires":["Redis Stack or Redis Enterprise (includes Search module)","Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability","Vector embeddings (from OpenAI, Anthropic, or other embedding models)"],"input_types":["vector index name (string)","vector dimension (integer)","similarity metric (e.g., 'cosine', 'euclidean')","document ID and embedding vector (for storage)","query vector (for search)"],"output_types":["similar documents with scores (array of objects)","index metadata (dimension, metric, entry count)","success/failure status"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_11","uri":"capability://data.processing.analysis.json.document.storage.and.path.based.querying","name":"json document storage and path-based querying","description":"Implements Redis JSON operations through MCP tools for storing and querying JSON documents using JSONPath expressions. Tools support JSON.SET (store document), JSON.GET (retrieve with path), and JSON.MGET (multi-document retrieval). The JSON module enables structured document storage with path-based access, allowing agents to store complex objects and query nested fields without serialization overhead. Agents can express intent like 'store configuration object' which maps to JSON.SET with automatic JSON validation.","intents":["Store complex configuration objects with nested structures","Query JSON documents using JSONPath expressions","Maintain structured data without serialization overhead","Update nested fields without full document replacement"],"best_for":["Applications storing complex nested objects (configurations, profiles, documents)","JSONPath-based querying without full document deserialization","Structured data requiring partial updates"],"limitations":["Requires Redis JSON module (not in Community Edition) — only available in Redis Enterprise or Redis Stack","JSONPath query capabilities limited compared to document databases — no aggregation or complex filtering","No schema validation — invalid JSON accepted without enforcement","Memory overhead for JSON storage — less efficient than binary formats"],"requires":["Redis Stack or Redis Enterprise (includes JSON module)","Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["JSON key (string)","JSON document (valid JSON object or array)","JSONPath expression (string, for queries)"],"output_types":["JSON document or path result (JSON object/array/value)","success/failure status"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_12","uri":"capability://automation.workflow.server.management.and.database.administration.operations","name":"server management and database administration operations","description":"Provides server management tools for database administration through MCP tools exposing INFO (server statistics), FLUSHDB (clear database), DBSIZE (count keys), and configuration operations. Tools enable agents to monitor Redis health, manage database state, and perform administrative tasks. Agents can express intent like 'check Redis health' which maps to INFO command, enabling observability and operational management without direct Redis CLI access.","intents":["Monitor Redis server health and performance metrics","Clear database or specific keys for testing and cleanup","Count keys or estimate memory usage","Perform administrative operations programmatically"],"best_for":["Operational monitoring and health checks for Redis instances","Database cleanup and maintenance operations","Administrative automation in multi-agent systems"],"limitations":["FLUSHDB is destructive — no undo capability, requires careful authorization","INFO output is unstructured text — requires parsing for programmatic use","No fine-grained access control — all management operations require same Redis authentication","DBSIZE counts all keys — expensive operation on large databases"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability","Appropriate Redis permissions (INFO, FLUSHDB, DBSIZE)"],"input_types":["command type (INFO, FLUSHDB, DBSIZE, etc.)","optional parameters (database index for FLUSHDB)"],"output_types":["server statistics (INFO output)","key count (integer)","success/failure status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_2","uri":"capability://tool.use.integration.singleton.redis.connection.pooling.with.cluster.support","name":"singleton redis connection pooling with cluster support","description":"Manages Redis connections through a RedisConnectionManager singleton pattern that handles both standalone Redis instances and Redis Cluster deployments. The connection manager maintains a pool of connections, handles SSL/TLS encryption, manages authentication via environment variables, and abstracts cluster topology discovery. All MCP tools reference the singleton, ensuring connection reuse and preventing connection exhaustion across concurrent agent requests.","intents":["Maintain efficient connection pooling across multiple concurrent agent requests to Redis","Support both standalone Redis and Redis Cluster deployments without tool-level changes","Secure Redis connections with SSL/TLS and authentication credentials","Automatically handle cluster node discovery and failover"],"best_for":["Production deployments with high concurrency from multiple AI agents","Organizations using Redis Cluster for high availability and horizontal scaling","Teams requiring encrypted Redis connections in regulated environments"],"limitations":["Singleton pattern creates global state — difficult to test with multiple Redis instances in same process","Connection pool size not exposed as configurable parameter — uses Redis client library defaults","Cluster failover handled by Redis client library — no custom failover policies or circuit breakers","SSL/TLS configuration requires environment variables — no programmatic certificate management"],"requires":["Python 3.9+","redis-py client library","Redis instance (standalone or cluster) with network accessibility","For SSL/TLS: REDIS_SSL environment variable and certificate paths","For authentication: REDIS_PASSWORD environment variable"],"input_types":["environment variables (REDIS_HOST, REDIS_PORT, REDIS_PASSWORD, REDIS_SSL, etc.)","Redis commands from MCP tools"],"output_types":["Redis connection object (internal)","operation results from Redis commands"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_3","uri":"capability://data.processing.analysis.string.key.value.storage.with.ttl.and.expiration","name":"string key-value storage with ttl and expiration","description":"Implements Redis string operations through MCP tools that support basic key-value storage with optional time-to-live (TTL) expiration. The set_string tool accepts key, value, and optional expiration parameters, automatically translating to Redis SET command with EX (seconds) or PX (milliseconds) options. Agents can express intent like 'cache this item' which maps to set_string with appropriate TTL, enabling automatic cache invalidation without explicit deletion logic.","intents":["Cache computation results or API responses with automatic expiration","Store session data with TTL for automatic cleanup","Implement rate limiting counters that expire after time windows","Store temporary state for multi-turn agent conversations"],"best_for":["Caching layers for AI agents processing expensive computations","Session management in multi-turn agent conversations","Rate limiting and quota tracking for agent API calls"],"limitations":["No atomic compare-and-set (CAS) operations — concurrent updates may lose data","TTL granularity limited to seconds or milliseconds — no sub-millisecond precision","No built-in compression — large values consume full memory without automatic optimization","Expiration is approximate — Redis may not evict keys immediately at TTL boundary"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["key (string)","value (string or serialized JSON)","expiration (optional, in seconds or milliseconds)"],"output_types":["success/failure status","retrieved string value (for get operations)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_4","uri":"capability://data.processing.analysis.hash.based.structured.data.storage.with.field.level.operations","name":"hash-based structured data storage with field-level operations","description":"Provides Redis hash operations through MCP tools for storing and manipulating structured data with named fields. Tools like hset, hget, hgetall, and hdel operate on hash keys, allowing agents to store objects as field-value pairs (e.g., storing user profiles with fields like 'name', 'email', 'created_at'). Hashes support TTL at the key level, enabling expiring structured records like sessions or temporary user data without serializing to JSON strings.","intents":["Store user profiles or session data with multiple named fields","Maintain structured configuration objects with field-level access","Implement object-like storage without JSON serialization overhead","Store session data with TTL for automatic cleanup"],"best_for":["Storing structured objects (user profiles, sessions, configurations) with field-level access","Applications requiring partial updates without full object serialization","Multi-field data that benefits from Redis's field-level operations"],"limitations":["No nested structures — hash fields are flat key-value pairs, no support for nested objects","Field values are strings — numeric operations require explicit conversion","No atomic multi-field updates — concurrent field modifications may lose data","TTL applies to entire hash key, not individual fields — cannot expire specific fields"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["hash key (string)","field names (strings)","field values (strings or serialized data)","optional TTL for entire hash"],"output_types":["field values (strings)","entire hash as key-value object","success/failure status"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_5","uri":"capability://data.processing.analysis.list.operations.with.push.pop.and.range.retrieval","name":"list operations with push/pop and range retrieval","description":"Implements Redis list operations through MCP tools supporting append (LPUSH/RPUSH), removal (LPOP/RPOP), and range retrieval (LRANGE). Lists maintain insertion order and support blocking operations, enabling use cases like task queues, activity logs, and sliding windows. Agents can express intent like 'add to queue' which maps to RPUSH, or 'get recent items' which maps to LRANGE with negative indices for tail elements.","intents":["Implement task queues for agent-driven workflows","Maintain activity logs or event histories with insertion order","Build sliding window data structures for time-series analysis","Implement blocking queues for producer-consumer patterns"],"best_for":["Task queue implementations for agent workflows","Activity logs and event histories requiring insertion order","Producer-consumer patterns with blocking operations"],"limitations":["No random access by index — retrieving middle elements requires LRANGE with full range","Blocking operations (BLPOP) not exposed as MCP tools — requires custom implementation","No built-in deduplication — duplicate items can accumulate in lists","Memory overhead for large lists — Redis stores entire list in memory without streaming"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["list key (string)","values to push (strings or serialized data)","range indices (for LRANGE operations)"],"output_types":["list elements (strings)","list length","success/failure status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_6","uri":"capability://data.processing.analysis.set.operations.with.membership.testing.and.set.algebra","name":"set operations with membership testing and set algebra","description":"Provides Redis set operations through MCP tools for managing unordered collections of unique elements. Tools support SADD (add members), SREM (remove members), SMEMBERS (retrieve all), SISMEMBER (test membership), and set algebra operations (SUNION, SINTER, SDIFF). Sets automatically deduplicate elements and support fast membership testing (O(1) average case), enabling use cases like tag management, user groups, and deduplication.","intents":["Manage tags or categories with automatic deduplication","Track user groups or permissions with set intersection/union","Implement deduplication for event streams or user interactions","Build recommendation systems using set intersection (common interests)"],"best_for":["Tag and category management with deduplication","User group and permission management with set operations","Recommendation systems using set intersection"],"limitations":["No ordering — sets are unordered, cannot retrieve elements by insertion order","Set algebra operations (SUNION, SINTER, SDIFF) not exposed as MCP tools — requires custom implementation","No element scoring or ranking — use sorted sets for ranked collections","Memory overhead for sparse sets — Redis stores all elements in memory"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["set key (string)","members to add/remove (strings)","member to test (string)"],"output_types":["set members (array of strings)","membership test result (boolean)","set cardinality (integer)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_7","uri":"capability://data.processing.analysis.sorted.set.operations.with.scoring.and.range.queries","name":"sorted set operations with scoring and range queries","description":"Implements Redis sorted set operations through MCP tools for managing ranked or scored collections. Tools support ZADD (add scored members), ZRANGE (retrieve by rank), ZRANGEBYSCORE (retrieve by score range), and ZREM (remove members). Sorted sets maintain elements in score order, enabling leaderboards, priority queues, and time-series data with automatic ordering. Agents can express intent like 'get top 10 items' which maps to ZRANGE with WITHSCORES.","intents":["Implement leaderboards with automatic ranking by score","Build priority queues with score-based ordering","Store time-series data with timestamps as scores","Implement rate limiting with sliding window counters"],"best_for":["Leaderboard and ranking systems","Priority queue implementations","Time-series data with automatic ordering","Rate limiting with sliding window counters"],"limitations":["Score precision limited to 64-bit floating point — may lose precision for very large numbers","Range queries by score (ZRANGEBYSCORE) not exposed as MCP tools — requires custom implementation","No multi-field scoring — only single numeric score per member","Memory overhead for large sorted sets — Redis stores all members in memory"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["sorted set key (string)","members with scores (member: string, score: float)","rank or score range parameters"],"output_types":["ranked members with scores (array of objects)","member count in range (integer)","success/failure status"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_8","uri":"capability://data.processing.analysis.stream.append.only.log.with.consumer.groups","name":"stream append-only log with consumer groups","description":"Provides Redis stream operations through MCP tools for building append-only event logs with consumer group support. Streams maintain insertion order, assign unique IDs, and support XADD (append), XRANGE (retrieve by ID range), and consumer group operations. Agents can express intent like 'store entire conversation in stream' which maps to XADD, enabling immutable event logs for audit trails, message queues, and conversation history without explicit ID management.","intents":["Store conversation history as immutable event log","Implement audit trails for compliance and debugging","Build message queues with consumer group support","Maintain activity streams with automatic ID generation"],"best_for":["Conversation history and audit logs requiring immutability","Message queue systems with consumer group support","Event sourcing architectures with append-only logs"],"limitations":["Consumer group operations (XREADGROUP) not exposed as MCP tools — requires custom implementation","Stream ID format (timestamp-sequence) may not match application ID requirements","No built-in retention policy — streams grow unbounded without explicit XTRIM","Memory overhead for large streams — Redis stores all entries in memory"],"requires":["Redis 5.0+ (streams introduced in 5.0)","Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability"],"input_types":["stream key (string)","entry data (key-value pairs as strings)","optional ID specification"],"output_types":["stream entries with IDs (array of objects)","entry count (integer)","success/failure status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__cap_9","uri":"capability://tool.use.integration.pub.sub.messaging.with.channel.subscriptions","name":"pub/sub messaging with channel subscriptions","description":"Implements Redis Pub/Sub operations through MCP tools for publish-subscribe messaging patterns. Tools support PUBLISH (send message to channel) and subscription management, enabling real-time message distribution to multiple subscribers. Agents can express intent like 'broadcast notification' which maps to PUBLISH, enabling event-driven architectures where agents publish events and other agents subscribe to channels without explicit message queuing.","intents":["Broadcast notifications or events to multiple agents","Implement real-time updates for multi-agent systems","Build event-driven architectures with loose coupling","Coordinate agent actions through message channels"],"best_for":["Real-time event distribution in multi-agent systems","Loose coupling between agent components","Broadcast notifications and system events"],"limitations":["No message persistence — subscribers must be connected to receive messages, lost messages not recoverable","No message ordering guarantees across multiple publishers","Subscription management (SUBSCRIBE, UNSUBSCRIBE) not exposed as MCP tools — requires custom implementation","No message filtering or pattern matching at Redis level — filtering must be application-level"],"requires":["Redis instance with network accessibility","RedisConnectionManager singleton initialized","MCP tool invocation capability","Separate connection for subscription (Pub/Sub blocks connection)"],"input_types":["channel name (string)","message content (string or serialized data)"],"output_types":["subscriber count (integer)","success/failure status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"redis-mcp-server__headline","uri":"capability://tool.use.integration.natural.language.interface.for.redis.operations","name":"natural language interface for redis operations","description":"The Redis MCP Server acts as a middleware that allows AI agents to interact with Redis databases using natural language queries, simplifying data operations without needing to know Redis syntax.","intents":["best Redis MCP server","MCP server for AI integration","natural language interface for Redis","Redis server for caching and messaging","Redis server for AI applications"],"best_for":["AI-driven applications","real-time data processing"],"limitations":[],"requires":[],"input_types":["natural language queries"],"output_types":["Redis operations"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":74,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Redis instance (standalone or cluster)","FastMCP framework","MCP-compatible AI platform (Claude Desktop, OpenAI Agents SDK, or custom MCP client)","For SSE: HTTP server infrastructure (reverse proxy, load balancer)","For Docker: Docker runtime and Redis network accessibility","MCP_TRANSPORT environment variable set to 'stdio', 'sse', or container-specific value","Redis Stack or Redis Enterprise (includes Search module)","Redis instance with network accessibility","RedisConnectionManager singleton initialized"],"failure_modes":["Translation quality depends on LLM's understanding of Redis semantics — complex multi-step operations may require explicit tool chaining","No built-in query optimization — agent may generate inefficient Redis command sequences","Limited to operations exposed as MCP tools — custom Redis modules or Lua scripts not directly accessible","SSE transport is unidirectional streaming — bidirectional operations require request-response polling","Docker deployment requires managing Redis connection credentials via environment variables — no secrets management built-in","Stdio transport limited to single client per process — requires process spawning for multiple concurrent agents","Requires Redis Search module (not available in Redis Community Edition) — only available in Redis Enterprise or Redis Stack","Vector dimension and similarity metric must be specified at index creation — cannot change without recreating index","Approximate nearest neighbor search may miss exact matches — trade-off between speed and accuracy","Index creation is blocking operation — large datasets may cause latency spikes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.52,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=redis-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=redis-mcp-server"}},"signature":"rj/D2WnSggrltzdID0zcK8J5v0yENY/aVelpKMoq5c60JSb+pJ4/RLyXiYcC1KPa1MfaFVllf82ew2ZBJ9kTAA==","signedAt":"2026-06-23T08:13:16.616Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/redis-mcp-server","artifact":"https://unfragile.ai/redis-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=redis-mcp-server","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"}}