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The server acts as a protocol bridge that translates MCP tool calls into native Elasticsearch REST API requests, handling authentication and transport protocol abstraction (stdio, HTTP, SSE) transparently. This enables LLM clients to discover and inspect the data landscape before executing queries.","intents":["I need to see what indices exist in my Elasticsearch cluster before querying","I want to understand the size and health of my indices programmatically","I need to list all available data sources that an LLM can query"],"best_for":["LLM-powered data exploration tools","Teams building natural language interfaces to Elasticsearch","Data engineers automating index discovery workflows"],"limitations":["Returns only index-level metadata; does not provide field-level statistics or cardinality estimates","No filtering or sorting capabilities — returns all indices regardless of naming patterns","Requires network connectivity to Elasticsearch cluster; no local caching of index metadata"],"requires":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials (API key, username/password, or mTLS certificate)","Network access from MCP server to Elasticsearch cluster"],"input_types":["none (no parameters required)"],"output_types":["structured JSON with index names, document counts, storage sizes, health status"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_1","uri":"capability://data.processing.analysis.field.mapping.retrieval.and.schema.inspection","name":"field mapping retrieval and schema inspection","description":"Retrieves Elasticsearch field mappings via the _mapping API, exposing the complete schema (field names, data types, analyzers, nested structures) for one or more indices. The server translates MCP tool parameters into Elasticsearch mapping requests and returns structured field metadata that LLMs can use to understand data structure before constructing queries. Supports inspection of nested fields, keyword vs text analysis, and custom analyzer configurations.","intents":["I need to know what fields are available in an index before writing a query","I want to understand the data types and analyzers configured for each field","I need to inspect nested object structures in my Elasticsearch documents"],"best_for":["LLM agents building dynamic Elasticsearch Query DSL","Teams automating schema-aware query generation","Data analysts exploring unfamiliar Elasticsearch indices"],"limitations":["Returns mapping definitions only; does not provide field cardinality, value distributions, or sample data","No support for runtime fields or computed fields in mapping response","Mapping changes require re-fetching; no change detection or versioning"],"requires":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials","Index must exist and be accessible"],"input_types":["index name (string)"],"output_types":["structured JSON with field definitions, types, analyzers, and nested structures"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_10","uri":"capability://automation.workflow.automated.dependency.management.and.security.updates","name":"automated dependency management and security updates","description":"The project uses Renovate for automated dependency management, scanning Cargo.toml for outdated dependencies and submitting pull requests weekly. This ensures the Rust codebase stays current with security patches and bug fixes in upstream libraries (Elasticsearch client, MCP protocol, async runtime). The automation reduces manual maintenance burden and improves security posture by catching vulnerable dependencies automatically.","intents":["I want to ensure Elasticsearch MCP stays secure with latest dependency patches","I need to keep the server updated without manual dependency management","I want to track security vulnerabilities in transitive dependencies"],"best_for":["Teams deploying Elasticsearch MCP in production environments","Organizations with security compliance requirements","DevOps teams managing open-source tool updates"],"limitations":["Automated updates may introduce breaking changes requiring code updates","Renovate requires GitHub repository configuration; not available for self-hosted deployments","Weekly update frequency may be too aggressive for some organizations","No automatic testing of dependency updates; manual review still required"],"requires":["GitHub repository with Renovate enabled","Cargo.toml with explicit dependency versions"],"input_types":["none (automated process)"],"output_types":["pull requests with dependency updates"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_2","uri":"capability://search.retrieval.elasticsearch.query.dsl.execution.with.full.text.search","name":"elasticsearch query dsl execution with full-text search","description":"Executes arbitrary Elasticsearch Query DSL queries via the _search API, supporting full-text search, filtering, aggregations, and complex boolean logic. The MCP server accepts Query DSL JSON payloads, translates them into Elasticsearch requests with proper authentication, and returns paginated results with hit counts and relevance scores. Supports all Elasticsearch query types (match, term, range, bool, aggregations) and handles response pagination through size/from parameters.","intents":["I need to execute a complex search query against Elasticsearch data","I want to run full-text search with filters and aggregations","I need to retrieve documents matching specific criteria and get result counts"],"best_for":["LLM agents building natural language search interfaces","Teams automating Elasticsearch query generation from user intent","Data exploration workflows requiring dynamic query construction"],"limitations":["No query validation or optimization — malformed DSL queries will fail at Elasticsearch level","Pagination limited by Elasticsearch defaults (typically 10,000 documents max without scroll API)","No built-in query result caching — each request hits the cluster","Aggregation results may be large; no automatic truncation or sampling"],"requires":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials","Index must exist and contain documents"],"input_types":["Elasticsearch Query DSL JSON (query, aggs, size, from parameters)"],"output_types":["structured JSON with hits array, total count, aggregation results, relevance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_3","uri":"capability://search.retrieval.es.ql.query.execution.for.sql.like.elasticsearch.queries","name":"es|ql query execution for sql-like elasticsearch queries","description":"Executes ES|QL (Elasticsearch SQL-like query language) queries via the _query API with ES|QL syntax support. The server translates ES|QL statements into Elasticsearch requests and returns tabular results. This capability bridges SQL-familiar users and LLMs to Elasticsearch by providing a SQL-like interface while leveraging Elasticsearch's distributed query engine. Supports ES|QL syntax including FROM, WHERE, GROUP BY, STATS, and other clauses.","intents":["I want to query Elasticsearch using SQL-like syntax instead of Query DSL","I need to perform aggregations and analytics using familiar SQL patterns","I want to generate Elasticsearch queries from natural language using SQL-like intermediate representation"],"best_for":["Teams with SQL-experienced analysts querying Elasticsearch","LLM agents that generate SQL more reliably than Query DSL","Data exploration workflows requiring SQL-like syntax"],"limitations":["ES|QL is a newer Elasticsearch feature; not all Query DSL functionality maps to ES|QL","No support for complex joins or cross-index queries","Performance characteristics differ from Query DSL; some queries may be slower","Limited to Elasticsearch 8.11+ for full ES|QL support"],"requires":["Elasticsearch 8.11+ (or 9.x)","Valid authentication credentials","ES|QL feature enabled in cluster"],"input_types":["ES|QL query string (SQL-like syntax)"],"output_types":["structured JSON with tabular results, column names, and data types"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_4","uri":"capability://data.processing.analysis.shard.distribution.and.cluster.topology.inspection","name":"shard distribution and cluster topology inspection","description":"Retrieves shard allocation information via the _cat/shards API, exposing how data is distributed across cluster nodes. The server returns shard IDs, node assignments, shard state (STARTED, RELOCATING, etc.), and storage sizes. This capability enables visibility into cluster health, data distribution, and potential bottlenecks. Useful for understanding cluster topology before executing large queries or diagnosing performance issues.","intents":["I need to understand how my data is distributed across cluster nodes","I want to check if shards are properly allocated and healthy","I need to diagnose cluster topology issues before running expensive queries"],"best_for":["Elasticsearch cluster operators and SREs","Teams building cluster health monitoring dashboards","Data engineers optimizing query routing and performance"],"limitations":["Returns only shard-level metadata; does not provide node resource utilization or query performance metrics","No real-time monitoring — returns point-in-time snapshot","Does not include shard replica information or replication lag","Requires cluster-level permissions to access shard data"],"requires":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials with cluster permissions","Network access to Elasticsearch cluster"],"input_types":["none (no parameters required)"],"output_types":["structured JSON with shard IDs, node assignments, states, and storage sizes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_5","uri":"capability://tool.use.integration.multi.protocol.transport.abstraction.stdio.http.sse","name":"multi-protocol transport abstraction (stdio, http, sse)","description":"The MCP server implements three transport protocols (stdio for desktop integration, HTTP for web services, SSE for real-time streaming) through a unified Rust architecture. The core MCP tool implementations are protocol-agnostic; transport is handled by a pluggable layer that translates between protocol-specific message formats and internal MCP structures. This allows the same server binary to be deployed in different environments (Claude Desktop, web services, containerized systems) without code changes.","intents":["I need to run the Elasticsearch MCP server in Claude Desktop using stdio protocol","I want to deploy the server as a containerized HTTP service for web-based LLM clients","I need real-time streaming of Elasticsearch results using SSE protocol"],"best_for":["Teams deploying Elasticsearch MCP across multiple environments","Organizations requiring both desktop and cloud deployments","Developers building MCP-compatible clients with different transport requirements"],"limitations":["SSE protocol is deprecated in newer MCP specifications; HTTP is preferred for new deployments","Stdio protocol limited to single-client connections; not suitable for multi-user scenarios","HTTP protocol requires additional security configuration (TLS, authentication) for production use","No automatic protocol negotiation — must be explicitly configured at startup"],"requires":["Rust runtime (binary is pre-compiled for Linux, macOS, Windows)","For HTTP: network connectivity and port binding permissions","For stdio: process communication capability (available in Claude Desktop, Goose)"],"input_types":["MCP protocol messages (JSON-RPC format)"],"output_types":["MCP protocol responses (JSON-RPC format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_6","uri":"capability://safety.moderation.authentication.abstraction.with.multiple.credential.types","name":"authentication abstraction with multiple credential types","description":"The server supports three Elasticsearch authentication methods (API key via ES_API_KEY, basic auth via ES_USERNAME/ES_PASSWORD, and mTLS certificates) through environment variable configuration. Authentication is handled at the connection layer, transparently applied to all Elasticsearch API calls. The server also supports SSL/TLS configuration with optional certificate verification bypass via ES_SSL_SKIP_VERIFY for development environments. This abstraction allows deployment in different security contexts without code changes.","intents":["I need to authenticate to Elasticsearch using API keys in production","I want to use basic authentication for development environments","I need to configure mTLS certificates for secure cluster communication"],"best_for":["Teams deploying Elasticsearch MCP in production with strict security requirements","Organizations using different authentication methods across environments","DevOps teams managing Elasticsearch cluster access control"],"limitations":["Credentials must be provided via environment variables; no support for credential files or secret managers","No credential rotation or refresh logic — requires server restart to update credentials","SSL/TLS certificate verification bypass (ES_SSL_SKIP_VERIFY) is insecure and should not be used in production","No support for SAML, OAuth, or other advanced authentication methods"],"requires":["Valid Elasticsearch credentials (API key, username/password, or mTLS certificate)","Environment variables properly configured before server startup","For mTLS: certificate files accessible to server process"],"input_types":["environment variables (ES_API_KEY, ES_USERNAME, ES_PASSWORD, ES_SSL_SKIP_VERIFY)"],"output_types":["authenticated Elasticsearch API requests (transparent to MCP clients)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_7","uri":"capability://automation.workflow.elasticsearch.version.compatibility.management.8.x.and.9.x","name":"elasticsearch version compatibility management (8.x and 9.x)","description":"The server explicitly supports Elasticsearch 8.x and 9.x versions, with API compatibility handling built into the Rust codebase. The server uses Elasticsearch client libraries that abstract version-specific API differences, ensuring tools work consistently across supported versions. This includes handling API changes, deprecated endpoints, and new features introduced in Elasticsearch 9.x while maintaining backward compatibility with 8.x clusters.","intents":["I need to use the same MCP server with both Elasticsearch 8.x and 9.x clusters","I want to ensure my LLM queries work regardless of cluster version","I need to migrate from Elasticsearch 8.x to 9.x without updating MCP server"],"best_for":["Organizations running multiple Elasticsearch versions","Teams managing Elasticsearch upgrades without downtime","Enterprises requiring version-agnostic tooling"],"limitations":["Some Elasticsearch 9.x-specific features may not be available when connecting to 8.x clusters","API changes between versions may cause tool failures if cluster version is not compatible","No automatic version detection — assumes cluster is compatible with supported versions","Older Elasticsearch versions (7.x and earlier) are not supported"],"requires":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials"],"input_types":["none (version compatibility is automatic)"],"output_types":["version-compatible Elasticsearch API responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_8","uri":"capability://automation.workflow.docker.containerization.and.cloud.deployment","name":"docker containerization and cloud deployment","description":"The server is distributed as a Docker image (docker.elastic.co/mcp/elasticsearch) built through Buildkite CI/CD pipeline, enabling containerized deployment in Kubernetes, Docker Compose, and cloud platforms. The Docker image includes the compiled Rust binary, environment variable configuration, and health check endpoints. This enables teams to deploy Elasticsearch MCP as a microservice alongside LLM applications without managing Rust compilation or dependencies.","intents":["I need to deploy Elasticsearch MCP as a Docker container in Kubernetes","I want to run the server alongside my LLM application using Docker Compose","I need to scale Elasticsearch MCP across multiple cloud instances"],"best_for":["Teams deploying LLM applications in containerized environments","Organizations using Kubernetes for microservice orchestration","DevOps teams managing cloud infrastructure"],"limitations":["Docker image is Linux-based; no native Windows or macOS container support","Requires Docker runtime or Kubernetes cluster; not suitable for serverless deployments","Image size includes Rust runtime; larger than minimal Python-based alternatives","No built-in health checks or liveness probes — must be configured in orchestration platform"],"requires":["Docker runtime or Kubernetes cluster","Network connectivity to Elasticsearch cluster","Environment variables configured for authentication"],"input_types":["Docker environment variables, port bindings, volume mounts"],"output_types":["running container with MCP server listening on configured port"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__cap_9","uri":"capability://automation.workflow.cross.platform.binary.distribution.via.github.actions","name":"cross-platform binary distribution via github actions","description":"The server is compiled for multiple platforms (Linux, macOS, Windows) through GitHub Actions CI/CD pipeline and distributed as pre-compiled binaries. This enables users to download and run the server without Rust toolchain installation. The build process compiles the Rust codebase for each platform, runs tests, and publishes binaries to GitHub releases. This approach eliminates dependency management and compilation time for end users.","intents":["I want to download a pre-compiled binary and run Elasticsearch MCP on my machine","I need to use the server on macOS, Linux, and Windows without compiling from source","I want to integrate the server into my LLM application without managing Rust dependencies"],"best_for":["Individual developers using Elasticsearch MCP with Claude Desktop","Teams deploying to multiple operating systems","Users without Rust development environment"],"limitations":["Binary size is larger than minimal Python implementations due to Rust runtime","Platform-specific binaries must be downloaded separately; no universal binary","Binaries are not signed; users must trust GitHub release distribution","No automatic updates; users must manually download new versions"],"requires":["Operating system matching binary platform (Linux, macOS, or Windows)","Execute permissions on downloaded binary"],"input_types":["none (binary is pre-compiled)"],"output_types":["executable binary ready to run"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elasticsearch-mcp-server__headline","uri":"capability://tool.use.integration.mcp.server.for.elasticsearch","name":"mcp server for elasticsearch","description":"A community-driven MCP server that facilitates natural language interactions with Elasticsearch, enabling users to manage indices and perform searches through a standardized protocol interface.","intents":["best MCP server for Elasticsearch","MCP server for natural language search","how to manage Elasticsearch indices with MCP","Elasticsearch integration with Model Context Protocol","MCP tools for Elasticsearch analytics"],"best_for":["developers seeking to integrate natural language processing with Elasticsearch"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":75,"verified":false,"data_access_risk":"high","permissions":["Elasticsearch 8.x or 9.x cluster","Valid authentication credentials (API key, username/password, or mTLS certificate)","Network access from MCP server to Elasticsearch cluster","Valid authentication credentials","Index must exist and be accessible","GitHub repository with Renovate enabled","Cargo.toml with explicit dependency versions","Index must exist and contain documents","Elasticsearch 8.11+ (or 9.x)","ES|QL feature enabled in cluster"],"failure_modes":["Returns only index-level metadata; does not provide field-level statistics or cardinality estimates","No filtering or sorting capabilities — returns all indices regardless of naming patterns","Requires network connectivity to Elasticsearch cluster; no local caching of index metadata","Returns mapping definitions only; does not provide field cardinality, value distributions, or sample data","No support for runtime fields or computed fields in mapping response","Mapping changes require re-fetching; no change detection or versioning","Automated updates may introduce breaking changes requiring code updates","Renovate requires GitHub repository configuration; not available for self-hosted deployments","Weekly update frequency may be too aggressive for some organizations","No automatic testing of dependency updates; manual review still required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.62,"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":"2026-06-17T09:51:04.691Z","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=elasticsearch-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=elasticsearch-mcp-server"}},"signature":"1ktpfOez/VkNZrABJCq9YmaKIgYR+1j7Ipa/doxKHLWB5AcgvsHVT7U4KQUWObtbXeSiE0WXvituDQGO8A+FBQ==","signedAt":"2026-06-23T15:47:16.764Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/elasticsearch-mcp-server","artifact":"https://unfragile.ai/elasticsearch-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=elasticsearch-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"}}