Supermaven ranks higher at 71/100 vs @tocharianou/mcp-server-kibana at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @tocharianou/mcp-server-kibana | Supermaven |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 25/100 | 71/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 10 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes arbitrary Kibana REST API calls through the Model Context Protocol, translating MCP tool invocations into HTTP requests to a Kibana instance. Implements request marshaling, response parsing, and error handling to bridge Claude/LLM clients with Kibana's native API endpoints, supporting authentication via API keys or basic auth configured at server initialization.
Unique: Implements MCP as a standardized protocol bridge to Kibana's REST API, allowing Claude and other MCP-compatible clients to treat Kibana as a native tool without custom integrations. Uses MCP's tool schema system to expose Kibana endpoints dynamically.
vs alternatives: Provides direct Kibana API access through MCP's standardized tool protocol, whereas custom Kibana integrations require bespoke code for each LLM platform and lack the composability of MCP's tool ecosystem.
Constructs and executes Elasticsearch queries through Kibana's query DSL interface, translating natural language or structured parameters into Elasticsearch Query Language (EQL) or JSON query syntax. Handles index selection, field mapping, aggregation setup, and result formatting to enable LLMs to perform complex searches without manual query syntax knowledge.
Unique: Bridges natural language query intent to Elasticsearch DSL through Kibana's query abstraction, allowing LLMs to construct valid queries without deep Elasticsearch syntax knowledge. Leverages Kibana's index pattern metadata to infer field types and valid operators.
vs alternatives: Abstracts Elasticsearch query complexity through Kibana's UI-driven query builder, whereas direct Elasticsearch clients require LLMs to generate raw DSL syntax, increasing error rates and requiring more context about cluster schema.
Fetches metadata about saved Kibana dashboards, visualizations, and saved searches, including panel definitions, data sources, and configuration. Enables LLMs to discover available dashboards, understand their structure, and reference them in conversations without requiring manual documentation or UI navigation.
Unique: Exposes Kibana's saved objects API through MCP tools, allowing LLMs to introspect dashboard structure and discover available visualizations without UI navigation. Caches metadata in MCP context to reduce repeated API calls.
vs alternatives: Provides programmatic access to dashboard metadata through MCP, whereas manual Kibana UI navigation requires human interaction and doesn't integrate with LLM workflows; direct Elasticsearch access lacks Kibana's abstraction of saved objects.
Retrieves Elasticsearch index pattern configurations and field mappings from Kibana, exposing field names, data types, and aggregation capabilities. Enables LLMs to understand the schema of available indices and construct valid queries without requiring external schema documentation or trial-and-error field exploration.
Unique: Exposes Kibana's index pattern API to provide schema-aware field discovery, allowing LLMs to understand Elasticsearch field types and constraints without manual schema documentation. Integrates field metadata into MCP tool context for query construction.
vs alternatives: Provides schema discovery through Kibana's abstraction layer, whereas direct Elasticsearch mapping APIs require parsing raw JSON and lack Kibana's field formatting and UI-friendly metadata; manual documentation is error-prone and requires constant updates.
Manages Kibana alerting rules and anomaly detection jobs, allowing LLMs to create, modify, and query alert configurations. Supports threshold-based alerts, anomaly detection rules, and integration with notification channels (email, Slack, webhooks) to enable automated incident response workflows triggered by observability data.
Unique: Exposes Kibana's alerting and anomaly detection APIs through MCP, enabling LLMs to programmatically create and manage alerts without UI interaction. Integrates with Kibana's action connectors to support multi-channel notifications.
vs alternatives: Provides alert management through Kibana's native alerting framework, whereas custom alert systems require building separate infrastructure; direct Elasticsearch monitoring lacks Kibana's UI-driven rule builder and action connector ecosystem.
Queries Elastic APM (Application Performance Monitoring) data through Kibana, retrieving transaction traces, service metrics, and error information. Supports filtering by service, transaction type, and time range to enable LLMs to analyze application performance and troubleshoot latency or error issues without manual APM UI navigation.
Unique: Integrates Kibana's APM app API to expose distributed tracing data through MCP, allowing LLMs to analyze transaction traces and service dependencies without manual APM UI interaction. Supports trace-level filtering and span aggregation.
vs alternatives: Provides APM data access through Kibana's abstraction, whereas direct Elasticsearch queries require knowledge of APM index structure and span schema; manual APM UI navigation doesn't integrate with LLM workflows.
Aggregates logs from Elasticsearch indices and performs pattern analysis (e.g., identifying common error messages, grouping by log level). Enables LLMs to summarize log data, identify trends, and extract actionable insights without requiring manual log parsing or regex pattern matching.
Unique: Leverages Kibana's aggregation framework to perform log pattern analysis, exposing common error messages and log trends through MCP without requiring LLMs to parse raw log text. Integrates with Elasticsearch's terms and significant_terms aggregations.
vs alternatives: Provides structured log analysis through Kibana's aggregation API, whereas manual log parsing requires regex or NLP; direct Elasticsearch queries require understanding aggregation syntax and field mappings.
Retrieves time-series metrics (CPU, memory, network, application-specific metrics) from Elasticsearch and formats them for visualization or analysis. Supports metric aggregation, downsampling, and time-window bucketing to enable LLMs to analyze infrastructure and application performance trends without manual metric query construction.
Unique: Exposes Kibana's metrics aggregation and visualization APIs through MCP, enabling LLMs to query time-series data with automatic bucketing and downsampling. Supports multi-metric comparisons and dimension-based filtering.
vs alternatives: Provides time-series metric access through Kibana's abstraction, whereas direct Elasticsearch queries require manual date histogram and aggregation setup; manual metric UI navigation doesn't integrate with LLM workflows.
+2 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs @tocharianou/mcp-server-kibana at 25/100. @tocharianou/mcp-server-kibana leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities