The Elasticsearch 7.x MCP Server provides an MCP protocol interface for interacting with Elasticsearch 7.x, enabling integration through any compatible MCP client.
Key capabilities:
Check connection status: Use es-ping to verify Elasticsearch server availability
Retrieve cluster information: Use es-info to get detailed information about the Elasticsearch cluster
Search documents: Use es-search to perform comprehensive search operations
Advanced search features: Support for aggregation queries, highlighting, sorting, filtering, and specifying source fields
Enables interaction with Elasticsearch 7.x instances, supporting basic operations like ping and info, as well as complete search functionality including aggregation queries, highlighting, sorting, and other advanced search features.
Utilizes environment variables through .env files to configure connection details for Elasticsearch, including host address, authentication credentials, and server port settings.
Supports connection to Kibana as part of an Elasticsearch deployment through the Docker Compose setup, providing visualization and management capabilities for Elasticsearch data.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Elasticsearch 7.x MCP Server search for 'error logs' in the application index from the last 24 hours"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server for Elasticsearch 7.x, providing compatibility with Elasticsearch 7.x versions.
Provides an MCP protocol interface for interacting with Elasticsearch 7.x
Supports basic Elasticsearch operations (ping, info, etc.)
Supports complete search functionality, including aggregation queries, highlighting, sorting, and other advanced features
Easily access Elasticsearch functionality through any MCP client
Related MCP server: Elasticsearch/OpenSearch MCP Server
Python 3.10+
Elasticsearch 7.x (7.17.x recommended)
To install Elasticsearch 7.x MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @imlewc/elasticsearch7-mcp-server --client claudepip install -e .The server requires the following environment variables:
ELASTIC_HOST: Elasticsearch host address (e.g., http://localhost:9200)
ELASTIC_USERNAME: Elasticsearch username
ELASTIC_PASSWORD: Elasticsearch password
MCP_PORT: (Optional) MCP server listening port, default 9999
Create a .env file and set ELASTIC_PASSWORD:
ELASTIC_PASSWORD=your_secure_passwordStart the services:
docker-compose up -dThis will start a three-node Elasticsearch 7.17.10 cluster, Kibana, and the MCP server.
You can use any MCP client to connect to the MCP server:
from mcp import MCPClient
client = MCPClient("localhost:9999")
response = client.call("es-ping")
print(response) # {"success": true}Currently supported MCP methods:
es-ping: Check Elasticsearch connection
es-info: Get Elasticsearch cluster information
es-search: Search documents in Elasticsearch index
# 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"]
})Clone the repository
Install development dependencies
Run the server: elasticsearch7-mcp-server
[License in LICENSE file]
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. Dates show when Glama detected each change.
v1.0.0es-infoes-pinges-searchEach 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.
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