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MCP Elasticsearch Server

Community

76·Strong

Overall Trust Score

Community-maintained MCP server for Elasticsearch search and analytics operations. Enables AI models to perform full-text search, aggregations, indexing, and data analytics on large-scale datasets. Essential for AI-powered search optimization, log analysis, and business intelligence workflows.

search
analytics
mcp
model-context-protocol
Version: 1.0.0
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

84
search accuracy
90
Methodology
Search relevance testing
Evidence
Elasticsearch Search
Highly accurate full-text search with relevance scoring
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
aggregation reliability
88
Methodology
Aggregation accuracy testing
Evidence
Elasticsearch Aggregations
Powerful aggregation framework with accurate analytics
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
large index performance
78
Methodology
Scalability testing
Evidence
Elasticsearch Performance
Performance degrades with very large indices; requires optimization
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
cluster stability
82
Methodology
Cluster stability testing
Evidence
Elasticsearch Cluster Health
Stable cluster operations with automatic shard management
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
error recovery
83
Methodology
Error handling testing
Evidence
Implementation Review
Handles Elasticsearch errors with retry and timeout management
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Security

69
authentication security
78
Methodology
Authentication mechanism review
Evidence
Elasticsearch Security
Supports API keys, basic auth, and security features (X-Pack)
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
credential exposure risk
62
Methodology
Credential security analysis
Evidence
MCP Security Model
Elasticsearch credentials stored locally; accessible to AI
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
query injection risk
65
Methodology
Injection vulnerability testing
Evidence
Security Analysis
AI can construct arbitrary queries and scripts
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
index modification risk
60
Methodology
Operation authorization testing
Evidence
Elasticsearch API
AI can index, update, and delete documents within permissions
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
index deletion risk
68
Methodology
Destructive operation testing
Evidence
Implementation Review
Can delete indices if permissions allow
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
audit logging
80
Methodology
Audit logging review
Evidence
Elasticsearch Audit Logging
Comprehensive audit logging available in X-Pack Security
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

67
document data exposure
63
Methodology
Data flow analysis
Evidence
MCP Data Flow
Search results and indexed documents sent to LLM provider
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
pii in logs
60
Methodology
PII exposure assessment
Evidence
Privacy Analysis
Log data and application logs may contain PII
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
field level security
72
Methodology
Field security assessment
Evidence
Elasticsearch Field-Level Security
Field-level security available in X-Pack but requires configuration
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
third party data sharing
68
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
Search results shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
index pattern exposure
70
Methodology
Index privacy assessment
Evidence
Privacy Analysis
Index names and mappings may reveal data structure
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

79
documentation quality
76
Methodology
Documentation completeness review
Evidence
Elasticsearch MCP Docs
Good documentation but community-maintained with evolving coverage
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
query visibility
82
Methodology
Query logging assessment
Evidence
MCP Protocol
All queries logged in MCP transaction logs
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
open source transparency
88
Methodology
Source code review
Evidence
GitHub Repository
Open source community implementation
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
api coverage clarity
70
Methodology
API documentation review
Evidence
MCP Server Documentation
Clear but incomplete documentation of supported Elasticsearch APIs
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

81
ease of setup
80
Methodology
Setup complexity assessment
Evidence
Setup Documentation
Requires Elasticsearch connection URL and credentials
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
search performance
82
Methodology
Performance benchmarking
Evidence
Elasticsearch Performance
Performance varies with index size and query complexity (typically 50-500ms)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
reliability
83
Methodology
Reliability analysis
Evidence
Elasticsearch Stability
Built on mature Elasticsearch client libraries
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
api coverage
80
Methodology
Feature coverage assessment
Evidence
Elasticsearch MCP Server
Covers search, aggregations, indexing, and index management
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
community support
75
Methodology
Community support assessment
Evidence
GitHub Community
Community-maintained with moderate activity
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •Highly accurate full-text search with relevance scoring
  • •Powerful aggregation framework for analytics and insights
  • •Built on mature Elasticsearch client libraries
  • •Excellent for log analysis and business intelligence
  • •Open source community implementation
  • •Comprehensive audit logging in X-Pack Security

⚠️ Limitations

  • •Search results and indexed documents exposed to LLM provider
  • •Log data may contain PII and sensitive information
  • •AI can modify and delete indices within permission scope
  • •Elasticsearch credentials accessible to AI
  • •Performance issues with very large indices without optimization
  • •Field-level security requires X-Pack and careful configuration

📊 Metadata

license: MIT
supported platforms:
0: All platforms with Node.js/Python
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
api dependency: Elasticsearch client libraries
authentication: API keys, Basic auth, X-Pack Security
first release: 2024-11
maintained by: Community

Use Case Ratings

code generation

80

Good for generating search queries and analytics dashboards

customer support

88

Excellent for searching support tickets and knowledge bases

content creation

75

Useful for content search and recommendation systems

data analysis

95

Excellent for log analysis, business intelligence, and data exploration

research assistant

92

Ideal for full-text research across large document collections

legal compliance

58

Risk of exposing legal documents; requires field-level security

healthcare

52

High risk of exposing patient data in logs and indices

financial analysis

62

Moderate risk; financial transaction data exposure concerns

education

88

Excellent for teaching search technologies and data analytics

creative writing

72

Useful for searching writing archives and research materials