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Improvado's MCP server connects Elasticsearch to your AI agent. Query indices, analyze log patterns, monitor cluster performance, and surface anomalies — all in plain English. Works with Claude, Cursor, ChatGPT, and any MCP-compatible tool.
Ask your AI agent for search trends, error patterns, log anomalies, or index statistics. Improvado translates natural language into Elasticsearch DSL queries — no JSON query syntax required.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Update index mappings, adjust refresh intervals, manage aliases, and push configuration changes through your AI agent without writing Elasticsearch API calls by hand.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set your AI agent to track cluster health, shard allocation, query latency, and index sizes. Get alerts before performance degrades or storage runs out.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Update index mappings, adjust refresh intervals, manage aliases, and push configuration changes through your AI agent without writing Elasticsearch API calls by hand.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
Elasticsearch Query DSL is powerful but complex. Analysts who need search behavior data, log insights, or document counts have to file requests to engineering. By the time they get answers, the operational window has passed.
Improvado's MCP server lets your AI agent translate natural language questions into Elasticsearch DSL queries automatically. Analysts get direct self-serve access without learning the query language.
Debugging a production issue using Kibana means knowing where logs are, building filters, setting time ranges, and interpreting visualizations — all under pressure. During incidents, every minute counts and Kibana slows you down.
Ask your AI agent to surface the relevant log entries directly. Describe the symptom — error type, time window, service name — and get a structured summary without opening Kibana.
Elasticsearch indices grow without anyone noticing until query latency spikes or disk space runs out. By then, the cluster is under stress. There's no simple alert system for gradual resource consumption trends.
Set up continuous index size and performance monitoring through your AI agent. Define thresholds once — get alerts when indices grow unexpectedly, latency trends upward, or shard counts fall outside the healthy range.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Improvado supports Elasticsearch 7.x and 8.x, including self-hosted clusters and Elastic Cloud deployments. OpenSearch (AWS fork) is also supported. Connect your cluster endpoint and credentials through the Improvado platform.
Yes. Improvado's AI layer converts natural language questions into Elasticsearch DSL automatically. You describe what you want, and the MCP server generates the appropriate query against your connected indices.
Yes. Improvado pulls cluster health metrics, shard allocation status, index sizes, and query performance data. Your AI agent can surface health summaries on demand or send scheduled alerts based on defined thresholds.
Yes. Improvado connects to your Elasticsearch cluster via encrypted transport. Cluster credentials are stored in Improvado's encrypted vault. Your AI agent queries data through Improvado's secure proxy — raw credentials are never exposed to the AI model.
Yes. Improvado MCP connects to Elasticsearch via the standard REST API, which is consistent across Elastic Cloud, AWS OpenSearch, and self-managed deployments. For self-managed clusters inside private networks, a self-hosted Improvado agent can be deployed within the same network segment to relay data without exposing the cluster to the public internet. Authentication via API key, username/password, and PKI certificates is supported.
AI agents can query index statistics, document counts, mapping metadata, and — where your indices contain structured logs or application events — analyze patterns such as error rate trends, top query terms, latency distributions, or user activity sequences. Teams using Elasticsearch as an application search or observability backend can ask questions about search quality, indexing throughput, or cluster health without writing Elasticsearch DSL queries manually.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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