Elasticsearch 7.x 버전과의 호환성을 제공하는 Elasticsearch 7.x용 MCP 서버입니다.
Elasticsearch 7.x와 상호 작용하기 위한 MCP 프로토콜 인터페이스를 제공합니다.
기본적인 Elasticsearch 작업(ping, info 등)을 지원합니다.
집계 쿼리, 강조 표시, 정렬 및 기타 고급 기능을 포함한 완전한 검색 기능을 지원합니다.
모든 MCP 클라이언트를 통해 Elasticsearch 기능에 쉽게 액세스하세요
Related MCP server: Elasticsearch/OpenSearch MCP Server
파이썬 3.10+
Elasticsearch 7.x(7.17.x 권장)
Smithery를 통해 Claude Desktop에 Elasticsearch 7.x MCP 서버를 자동으로 설치하려면:
지엑스피1
pip install -e .서버에는 다음과 같은 환경 변수가 필요합니다.
ELASTIC_HOST : Elasticsearch 호스트 주소(예: http://localhost:9200 )
ELASTIC_USERNAME : Elasticsearch 사용자 이름
ELASTIC_PASSWORD : Elasticsearch 비밀번호
MCP_PORT : (선택 사항) MCP 서버 수신 포트, 기본값 9999
.env 파일을 만들고 ELASTIC_PASSWORD 설정합니다.
ELASTIC_PASSWORD=your_secure_password서비스 시작:
docker-compose up -d이렇게 하면 3노드 Elasticsearch 7.17.10 클러스터, Kibana 및 MCP 서버가 시작됩니다.
MCP 서버에 연결하려면 모든 MCP 클라이언트를 사용할 수 있습니다.
from mcp import MCPClient
client = MCPClient("localhost:9999")
response = client.call("es-ping")
print(response) # {"success": true}현재 지원되는 MCP 방법:
es-ping : Elasticsearch 연결 확인
es-info : Elasticsearch 클러스터 정보 가져오기
es-search : Elasticsearch 인덱스에서 문서 검색
# Basic search
search_response = client.call("es-search", {
"index": "my_index",
"query": {
"match": {
"title": "search keywords"
}
},
"size": 10,
"from": 0
})# Aggregation query
agg_response = client.call("es-search", {
"index": "my_index",
"size": 0, # Only need aggregation results, no documents
"aggs": {
"categories": {
"terms": {
"field": "category.keyword",
"size": 10
}
},
"avg_price": {
"avg": {
"field": "price"
}
}
}
})# Advanced search with highlighting, sorting, and filtering
advanced_response = client.call("es-search", {
"index": "my_index",
"query": {
"bool": {
"must": [
{"match": {"content": "search term"}}
],
"filter": [
{"range": {"price": {"gte": 100, "lte": 200}}}
]
}
},
"sort": [
{"date": {"order": "desc"}},
"_score"
],
"highlight": {
"fields": {
"content": {}
}
},
"_source": ["title", "date", "price"]
})저장소를 복제합니다
개발 종속성 설치
서버를 실행합니다: elasticsearch7-mcp-server
[LICENSE 파일의 라이센스]
Get Elasticsearch info
| Name | Required | Description | Default |
|---|---|---|---|
| req | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only states the action ('Get') without explaining what 'info' entails (e.g., cluster health, node details, version), whether it's read-only or has side effects, or any rate limits or authentication needs. This leaves critical behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, with no wasted language. It's front-loaded with the core action and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (1 parameter with nested object, no output schema, no annotations), the description is completely inadequate. It doesn't explain what information is returned, how to use the parameter, or behavioral aspects, leaving the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 1 parameter ('req') with 0% description coverage, and the tool description provides no information about parameters. The description doesn't explain what 'req' should contain or how to structure it, failing to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get Elasticsearch info' clearly states the action (get) and resource (Elasticsearch info), making the basic purpose understandable. However, it's vague about what specific information is retrieved and doesn't distinguish this tool from its sibling 'es-ping' (which likely checks connectivity) or 'es-search' (which searches data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, appropriate contexts, or exclusions. With sibling tools like 'es-ping' and 'es-search' available, the agent receives no help in choosing between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ping Elasticsearch server
| Name | Required | Description | Default |
|---|---|---|---|
| req | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action 'ping' without explaining what that entails—whether it's a health check, connectivity test, returns status codes, or has side effects. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, front-loading the essential action and target. There is no wasted language, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a ping operation (which could involve network checks, authentication, or status returns), no annotations, 0% schema coverage, and no output schema, the description is inadequate. It doesn't provide enough context for an agent to understand how to use the tool effectively or what to expect in return.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, and the description provides no information about the single parameter 'req'. It doesn't explain what 'req' should contain, its purpose, or how it affects the ping operation, failing to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'ping' and the target resource 'Elasticsearch server', making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'es-info' or 'es-search', but the action is specific enough to understand what it does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'es-info' or 'es-search'. The description only states what it does, not when it should be used or what context it applies to.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search documents in Elasticsearch index
| Name | Required | Description | Default |
|---|---|---|---|
| req | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action without details on permissions, rate limits, response format, or side effects. For a search tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for the basic information it conveys, though it could be more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (search operation with nested object parameter), lack of annotations, no output schema, and 0% schema coverage, the description is completely inadequate. It fails to provide necessary context for correct tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no information about the single parameter 'req' (an object). It doesn't explain what 'req' should contain, such as query fields or options, leaving parameters completely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('Search') and resource ('documents in Elasticsearch index'), which gives a basic understanding of purpose. However, it lacks specificity about what kind of search (e.g., full-text, filtered) and doesn't differentiate from sibling tools like 'es-info' or 'es-ping', making it vague rather than clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, context for searching, or exclusions, leaving the agent with no usage instructions beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0es-infoes-pinges-searchScored across 3 tools
Each tool has a clearly distinct purpose: es-info retrieves server information, es-ping checks connectivity, and es-search performs document searches. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.
All tool names follow a consistent 'es-' prefix and underscore-separated verb pattern (es-info, es-ping, es-search). This predictable naming convention enhances readability and usability across the tool set.
With only 3 tools, the server feels thin for an Elasticsearch domain, which typically involves operations like indexing, updating, deleting, and aggregating documents. The count is too low to cover the expected scope of a database/search engine server.
The tool set is severely incomplete for Elasticsearch functionality. It lacks essential CRUD operations (e.g., create, update, delete documents), index management, and query features beyond basic search, which will likely cause agent failures in handling typical database tasks.
Agent-driven search: build, import, tune, search, and score result quality — all over MCP.
A paid remote MCP for Context7 MCP docs, built to return verdicts, receipts, usage logs, and audit-r
MCP server for searching Airweave collections with natural language queries.
