{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-couchbase","slug":"couchbase","name":"Couchbase","type":"mcp","url":"https://github.com/Couchbase-Ecosystem/mcp-server-couchbase","page_url":"https://unfragile.ai/couchbase","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-couchbase__cap_0","uri":"capability://text.generation.language.natural.language.query.translation.to.n1ql","name":"natural language query translation to n1ql","description":"Converts natural language questions into Couchbase N1QL (SQL-like query language) statements through LLM-powered semantic understanding. The MCP server acts as an intermediary that parses user intent, constructs appropriate N1QL syntax with proper bucket/scope/collection references, and executes against connected Couchbase clusters. This enables non-SQL developers to query document databases using conversational language without learning N1QL syntax.","intents":["Query Couchbase data using plain English instead of writing N1QL","Explore cluster schema and data structure through conversational questions","Generate ad-hoc reports by asking natural language questions about stored documents","Debug data issues by querying specific documents or aggregations conversationally"],"best_for":["Data analysts and non-technical stakeholders querying Couchbase clusters","LLM application developers building conversational data interfaces","Teams migrating from REST APIs to natural language data access patterns"],"limitations":["Complex multi-join queries may require iterative refinement or explicit N1QL fallback","LLM hallucination risk for field names/bucket structures not in training data — requires schema context injection","No built-in query optimization — generated N1QL may be less efficient than hand-tuned queries","Ambiguous natural language questions may produce multiple valid N1QL interpretations without disambiguation"],"requires":["Couchbase Server 7.0+ with N1QL query service enabled","MCP client implementation (Claude Desktop, custom agent framework, etc.)","Network connectivity to Couchbase cluster (direct or via SSH tunnel)","Cluster credentials with query execution permissions"],"input_types":["natural language text (English)","conversational follow-up questions with context"],"output_types":["N1QL query string","query results (JSON documents)","result metadata (row count, execution time)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_1","uri":"capability://data.processing.analysis.couchbase.cluster.schema.introspection.and.documentation","name":"couchbase cluster schema introspection and documentation","description":"Automatically discovers and catalogs Couchbase cluster structure including buckets, scopes, collections, indexes, and document schemas through direct cluster API calls. The MCP server queries system catalogs and samples documents to build a schema model that can be injected into LLM context, enabling accurate natural language query generation and reducing hallucination about field names and data structures.","intents":["Understand the structure of an unfamiliar Couchbase cluster without manual documentation","Generate accurate schema context for LLM prompts to improve query translation","Validate that required fields/collections exist before attempting queries","Export cluster schema for documentation or migration planning"],"best_for":["DevOps and data engineers onboarding new Couchbase clusters","LLM application developers building context-aware query agents","Teams maintaining multiple Couchbase environments with evolving schemas"],"limitations":["Schema inference from document sampling may miss rare fields or optional attributes","Large clusters with millions of documents may have slow introspection due to full-table scans","No tracking of schema evolution over time — captures point-in-time snapshot only","Requires elevated cluster credentials (admin or query service access) to read system catalogs"],"requires":["Couchbase Server 7.0+ with Admin or Query service credentials","Network access to cluster management API (port 8091 by default)","Sufficient cluster permissions to query system:buckets, system:scopes, system:collections"],"input_types":["cluster connection parameters (host, port, credentials)","optional: specific bucket/scope/collection names to introspect"],"output_types":["structured schema JSON (buckets, scopes, collections, indexes)","sample documents per collection","field type inference (string, number, boolean, nested objects)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_2","uri":"capability://data.processing.analysis.direct.n1ql.query.execution.with.result.streaming","name":"direct n1ql query execution with result streaming","description":"Executes pre-written or generated N1QL queries directly against Couchbase clusters and streams results back through the MCP protocol. The server maintains connection pooling to the cluster, handles query timeouts and retries, and formats results as JSON for consumption by LLM agents or client applications. Supports parameterized queries to prevent injection attacks and enable safe dynamic query construction.","intents":["Execute N1QL queries from LLM agents without building custom database drivers","Stream large result sets without loading entire responses into memory","Run parameterized queries with variable substitution for safe dynamic query building","Retrieve specific documents by key or query criteria for downstream processing"],"best_for":["LLM agents and autonomous systems querying Couchbase programmatically","Data pipeline builders integrating Couchbase with LLM workflows","Applications requiring safe, parameterized query execution from untrusted input"],"limitations":["Query timeout defaults may need tuning for long-running aggregations or large result sets","No built-in pagination — large result sets must be handled by client-side chunking","Streaming results require MCP client support for streaming protocol (not all clients implement this)","Query performance depends on cluster indexing — unindexed queries may timeout or consume excessive resources"],"requires":["Couchbase Server 7.0+ with N1QL query service enabled","Query service credentials with execute permissions on target buckets","Network connectivity to cluster query service (port 8093 by default)","MCP client with streaming support for large result sets"],"input_types":["N1QL query string (plain or parameterized with $1, $2 placeholders)","query parameters array (for parameterized queries)","optional: timeout in milliseconds, consistency level"],"output_types":["JSON array of result documents","query metadata (rows affected, execution time, plan)","error messages with N1QL error codes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_3","uri":"capability://data.processing.analysis.document.crud.operations.with.consistency.control","name":"document crud operations with consistency control","description":"Provides atomic read, insert, update, and delete operations on individual Couchbase documents through MCP tool bindings. Supports optimistic concurrency control via CAS (Compare-And-Swap) tokens to prevent lost updates in concurrent scenarios, and allows specification of consistency levels (eventual, strong) for read operations. Operations are transactional at the document level and can be chained in agent workflows.","intents":["Insert or update individual documents from LLM agent decisions without manual database code","Safely update documents in concurrent environments using CAS tokens to prevent conflicts","Delete documents based on agent reasoning or user requests","Read specific documents by key for validation or context before mutations"],"best_for":["Autonomous agents making data mutations based on LLM reasoning","Multi-user applications requiring optimistic concurrency control","Workflows that need atomic document-level operations without transactions"],"limitations":["No multi-document transactions — each operation is atomic only at document level","CAS conflicts require retry logic in agent code — no automatic retry built-in","Large document updates may exceed network timeouts if documents are very large","No built-in schema validation — accepts any JSON structure without validation"],"requires":["Couchbase Server 7.0+","Credentials with read/write/delete permissions on target collections","Network connectivity to cluster data service (port 11210 by default)","Document key or query criteria to identify target documents"],"input_types":["document key (string)","document content (JSON object)","optional: CAS token (for updates)","optional: TTL in seconds (for expiration)"],"output_types":["operation result (success/failure)","updated document with new CAS token","error details (conflict, not found, permission denied)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_4","uri":"capability://search.retrieval.full.text.search.fts.query.execution","name":"full-text search (fts) query execution","description":"Executes Couchbase Full-Text Search queries through MCP tools, enabling semantic and keyword-based document retrieval across large collections. The server translates search criteria into FTS query syntax, handles faceting and result ranking, and returns ranked results with relevance scores. Supports complex queries including boolean operators, phrase search, and field-specific search within indexed documents.","intents":["Search for documents by keyword or semantic similarity without writing FTS query syntax","Find relevant documents for LLM context injection based on user queries","Implement faceted search interfaces with category counts and filters","Rank search results by relevance for improved context selection in agent workflows"],"best_for":["LLM applications needing semantic document retrieval for RAG workflows","Search-heavy applications built on Couchbase with ranking requirements","Teams implementing conversational search interfaces over document collections"],"limitations":["Requires pre-built FTS indexes — unindexed collections cannot be searched","Index building and updates add latency for real-time data — eventual consistency only","FTS query syntax complexity may require manual query construction for advanced use cases","Relevance scoring depends on index configuration — tuning required for domain-specific ranking"],"requires":["Couchbase Server 7.0+ with Full-Text Search service enabled","Pre-built FTS indexes on target collections","Credentials with FTS query permissions","Network connectivity to FTS service (port 8094 by default)"],"input_types":["search query string (keywords, phrases, boolean operators)","optional: field names to search within","optional: facet fields, result limit, offset for pagination"],"output_types":["ranked result documents with relevance scores","facet counts (category breakdowns)","total hits count for pagination"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_5","uri":"capability://data.processing.analysis.batch.document.operations.with.error.handling","name":"batch document operations with error handling","description":"Executes multiple document operations (inserts, updates, deletes) in a single batch request with per-document error handling and partial success reporting. The server optimizes batch operations for throughput using connection pooling and pipelining, and returns detailed results indicating which operations succeeded and which failed with specific error reasons. Useful for bulk data loading or multi-document mutations from agent workflows.","intents":["Insert or update multiple documents in a single operation for efficiency","Perform bulk data migrations or imports from external sources","Handle partial failures gracefully when some documents fail while others succeed","Optimize throughput for high-volume document operations from agent workflows"],"best_for":["Data pipeline builders performing bulk Couchbase operations","LLM agents executing multiple mutations as part of complex workflows","Applications requiring efficient bulk inserts without transaction overhead"],"limitations":["No transactional guarantees — partial batch success is possible and must be handled","Batch size limits may apply depending on cluster configuration and network","Error handling is per-document — no rollback mechanism for failed operations","CAS tokens not supported in batch operations — no optimistic concurrency control"],"requires":["Couchbase Server 7.0+","Credentials with write/delete permissions on target collections","Network connectivity to cluster data service","Batch size under cluster limits (typically 1000+ documents per batch)"],"input_types":["array of document operations (insert/update/delete)","each operation includes: key, content (for insert/update), optional TTL"],"output_types":["batch result summary (total, succeeded, failed)","per-document results with success/failure status and error details","execution time and throughput metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_6","uri":"capability://data.processing.analysis.query.result.caching.and.result.set.pagination","name":"query result caching and result set pagination","description":"Caches N1QL query results in memory with configurable TTL and provides cursor-based pagination for large result sets. The server maintains a result cache indexed by query hash, enabling repeated queries to return cached results without re-executing against the cluster. Pagination uses cursor tokens to maintain position across multiple requests, avoiding offset-based inefficiency for large datasets.","intents":["Reduce cluster load by caching frequently-executed queries","Paginate large result sets efficiently without re-executing queries","Maintain query result consistency across multiple agent requests","Improve response latency for repeated queries in conversational workflows"],"best_for":["Conversational agents executing similar queries repeatedly","Applications with read-heavy workloads and acceptable staleness windows","Teams optimizing cluster resource usage and query costs"],"limitations":["Cache staleness — cached results may not reflect recent mutations","Memory overhead for large result sets — cache size must be managed","Cache invalidation is time-based only — no event-driven invalidation on data changes","Cursor tokens are server-specific — cannot be shared across server instances"],"requires":["Couchbase Server 7.0+","MCP client supporting stateful pagination (cursor tokens)","Configurable cache TTL and size limits"],"input_types":["N1QL query string","optional: cache TTL in seconds","optional: page size for pagination","optional: cursor token for subsequent pages"],"output_types":["result page (array of documents)","cursor token for next page","metadata (total rows, current page, cache hit indicator)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-couchbase__cap_7","uri":"capability://automation.workflow.cluster.health.monitoring.and.diagnostic.reporting","name":"cluster health monitoring and diagnostic reporting","description":"Monitors Couchbase cluster health by querying node status, service availability, bucket statistics, and query performance metrics. The MCP server exposes cluster diagnostics as tools that agents can invoke to validate cluster state before executing queries, detect performance issues, or report health status. Includes metrics like memory usage, replication lag, and query queue depth.","intents":["Validate cluster health before executing critical queries or mutations","Diagnose performance issues by checking node status and service availability","Monitor replication lag and data consistency across cluster nodes","Report cluster metrics for observability and alerting workflows"],"best_for":["Autonomous agents requiring cluster health validation before operations","DevOps teams building observability and alerting on Couchbase","Applications needing graceful degradation based on cluster health"],"limitations":["Requires admin credentials to access full cluster diagnostics","Metrics are point-in-time snapshots — no historical trending without external storage","Health thresholds are configurable but require domain knowledge to tune","Some metrics may be unavailable on older Couchbase versions"],"requires":["Couchbase Server 7.0+","Admin or monitoring credentials","Network access to cluster management API (port 8091)"],"input_types":["optional: specific bucket or node to monitor","optional: metric categories to retrieve"],"output_types":["cluster health status (healthy/degraded/critical)","per-node status (up/down, services running)","bucket statistics (item count, memory usage, replication lag)","query performance metrics (queue depth, latency percentiles)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Couchbase Server 7.0+ with N1QL query service enabled","MCP client implementation (Claude Desktop, custom agent framework, etc.)","Network connectivity to Couchbase cluster (direct or via SSH tunnel)","Cluster credentials with query execution permissions","Couchbase Server 7.0+ with Admin or Query service credentials","Network access to cluster management API (port 8091 by default)","Sufficient cluster permissions to query system:buckets, system:scopes, system:collections","Query service credentials with execute permissions on target buckets","Network connectivity to cluster query service (port 8093 by default)","MCP client with streaming support for large result sets"],"failure_modes":["Complex multi-join queries may require iterative refinement or explicit N1QL fallback","LLM hallucination risk for field names/bucket structures not in training data — requires schema context injection","No built-in query optimization — generated N1QL may be less efficient than hand-tuned queries","Ambiguous natural language questions may produce multiple valid N1QL interpretations without disambiguation","Schema inference from document sampling may miss rare fields or optional attributes","Large clusters with millions of documents may have slow introspection due to full-table scans","No tracking of schema evolution over time — captures point-in-time snapshot only","Requires elevated cluster credentials (admin or query service access) to read system catalogs","Query timeout defaults may need tuning for long-running aggregations or large result sets","No built-in pagination — large result sets must be handled by client-side chunking","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.037Z","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=couchbase","compare_url":"https://unfragile.ai/compare?artifact=couchbase"}},"signature":"uA+BP3l7bYm7dJckLuj4lsqDGtYvrk1b+QOt0cYmeeeBVRLygBlyyJpxrgMHfae0NvD+EARsVo1G+Vci1KnIDQ==","signedAt":"2026-06-22T12:33:33.731Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/couchbase","artifact":"https://unfragile.ai/couchbase","verify":"https://unfragile.ai/api/v1/verify?slug=couchbase","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"}}