MCP Server for connecting to the Elasticsearch cluster directly from any MCP Client (such as Claude Desktop, Cursor).
[!IMPORTANT] This fork only supports Elasticsearch 7.x. It includes the
@elastic/elasticsearch7.17 client, and the product check for this client accepts servers older than 7.14. If you are using an Elasticsearch 8.x cluster, use the upstream project @awesome-ai/elasticsearch-mcp forked from here. The 8.x client cannot communicate with a 7.x server, and vice versa.
This server lets agents connect to your Elasticsearch data using the Model Context Protocol. Use natural language to interact with your Elasticsearch index in conversational form.
The tools come in three sets. Only the first set is always exposed; the other two are opt-in via environment variables, so you can provide diagnostics in production without enabling deletion features. Gating is implemented at tool registration: if a tool is disabled, it is not returned in tools/list, so the model cannot call it and incurs no context cost.
elasticsearch_health: cluster health, optionally down to the index level
cluster_info: cluster name, Elasticsearch version, and build flavor
index names: list indexes, optionally filtered by an Elasticsearch wildcard (log-*)
create_index: create an index, optionally with settings and mappings
reindex: copy an index, optionally filtered by a query or transformed with a script
get_aliases: show which aliases point to which indexes
get_mappings: shows the fields in an index as dot‑separated paths with their types, followed by the raw mapping
create_mapping: create or update the mapping of an index
search: run a query DSL search, injecting highlighting across all text fields — including nested ones — unless the query provides its own highlight
count: count matching documents without transferring them
get_document: fetch a document by ID
bulk: index multiple documents at once
create_index_template: create or update a composable index template
get_index_template: read index templates
get_task: get the progress of a long‑running task, such as the one returned by reindex
ES_ADMIN_TOOLS=true — Diagnostics (read-only)These tools only read data, so it is safe to enable them in production — this is the point of this set: a model, without someone logging into the cluster, can explain why an index is unhealthy.
explain_allocation: explain why a shard is unassigned, including each allocator's decision
list_shards: show shard‑level state, starting with the replicas that are not STARTED
list_nodes: heap, CPU, load, and disk pressure for each node
get_index_stats: per‑index counters — size, segments, indexing, search, merges
get_index_settings: an index's settings (refresh_interval, replicas, read‑only block)
get_cluster_settings: cluster settings that were overridden at runtime
list_tasks: what the cluster is currently running
ES_ALLOW_DESTRUCTIVE=true — irreversibleDesigned for a staging environment, off by default so production cannot access them at all.
delete_index: delete an index and its data
delete_document: delete a document by ID
delete by query: delete all documents matching a query — asynchronously, returns a task ID and continues in the background
delete_index_template: delete an index template
Even when this flag is on, these tools refuse wildcard patterns, comma‑separated lists, *, and _all: they operate only on one named index at a time. A model that mistakes logs-* for a single index gets a refusal instead of an emptied cluster.
The MCP client analyzes your request and determines which Elasticsearch operations are needed.
The MCP server performs those operations (listing indexes, fetching mappings, running searches).
The MCP client processes the results and presents them in a friendly format.
Related MCP server: Elasticsearch 7.x MCP Server
An Elasticsearch 7.x instance (tested against 7.8; the 7.17 client supports 6.8 through 7.x)
Elasticsearch credentials — an API key, or a username and password
An MCP client: Claude Code, Claude Desktop, Codex, Cursor, or anything else that speaks MCP over stdio.
[!IMPORTANT] This package is published to GitHub Packages, not npmjs.com, and GitHub Packages requires a token even for public packages. Until you add one, every install below fails with a 401. Put it in your user‑level
~/.npmrc:@agrica:registry=https://npm.pkg.github.com //npm.pkg.github.com/:_authToken=YOUR_GITHUB_TOKEN
YOUR_GITHUB_TOKENis a personal access token with theread:packagesscope.Keep it in your
~/.npmrc, not in a project file — a token committed to a repository is a leaked token, and some package managers refuse to read one from a project file.
Every example below sets ES_HOST and ES_API_KEY. To use Basic Auth instead, swap in ES_USERNAME/ES_PASSWORD. Add ES_ADMIN_TOOLS=true to enable the diagnostic tools, and set ES_INSTANCE_LABEL when you declare more than one instance — see Configuration Options.
claude mcp add elasticsearch7 \
--env ES_HOST=https://your-cluster:9200 \
--env ES_API_KEY=your-api-key \
--env ES_ADMIN_TOOLS=true \
-- npx -y @agrica/elasticsearch7-mcpThen /mcp in a session lists the server and its tools.
Two details that are easy to get wrong:
Everything after -- is the command that runs the server; without it, Claude Code would try to parse -y as one of its own flags.
Do not place the server name directly after --env — the CLI reads it as another KEY=value pair and rejects it. In the example above, the server name comes first, which is why it works.
The server is added at the local scope, so it only loads in the current project. Add --scope user to make it global, or --scope project to write it into .mcp.json and share it with your team. Be aware that a committed .mcp.json would still contain your API key, so prefer a user scope for credentials.
Edit claude_desktop_config.json — Settings → Developer → Edit Config opens it,
or locate it at %APPDATA%\Claude\ on Windows and ~/Library/Application Support/Claude/ on macOS:
{
"mcpServers": {
"elasticsearch7": {
"command": "npx",
"args": ["-y", "@agrica/elasticsearch7-mcp"],
"env": {
"ES_HOST": "https://your-cluster:9200",
"ES_API_KEY": "your-api-key",
"ES_ADMIN_TOOLS": "true"
}
}
}
}Restart Claude Desktop afterwards; it only reads that file at startup.
codex mcp add elasticsearch7 \
--env ES_HOST=https://your-cluster:9200 \
--env ES_API_KEY=your-api-key \
-- npx -y @agrica/elasticsearch7-mcpOr write it manually into ~/.codex/config.toml. Note that Codex uses mcp_servers for which the table assignment is mcp_servers with an underscore, and the environment is placed in its own sub‑table rather than inline:
[mcp_servers.elasticsearch7]
command = "npx"
args = ["-y", "@agrica/elasticsearch7-mcp"]
[mcp_servers.elasticsearch7.env]
ES_HOST = "https://your-cluster:9200"
ES_API_KEY = "your-api-key"
ES_ADMIN_TOOLS = "true"/mcp inside Codex confirms that the server is loaded.
The server is a plain stdio MCP server, so anything on the MCP client list works. It needs three things: the command npx, the arguments -y @agrica/elasticsearch7-mcp, and the ES_* variables in the environment. It never listens on a port and writes nothing but MCP protocol to stdout — diagnostics go to stderr.
The Elasticsearch MCP Server supports configuration options for connecting to Elasticsearch:
[!NOTE] You must provide either an API key or both a username and password for authentication.
Environment Variable | Description | Required |
| Your Elasticsearch instance URL(s) — supports a single URL or a comma‑separated list (also supports the legacy | Yes |
| Elasticsearch API key for authentication (also supports legacy | No |
| Elasticsearch username for basic authentication (legacy | No |
| Elasticsearch password for basic authentication (legacy | No |
| Path to a custom CA certificate for Elasticsearch SSl/TLS (legacy | No |
| Per‑request timeout in milliseconds. Default | No |
| Number of retries per request. Default | No |
| Upper limit for a single tool result. Default | No |
| Free‑text name for this deployment, e.g. | No |
|
| No |
|
| No |
[!WARNING]
ES_ADMIN_TOOLSandES_ALLOW_DESTRUCTIVEhave no un‑prefixed legacy alias, unlike the connection variables above. This is intentional: a bareADMIN_TOOLSorALLOW_DESTRUCTIVEin the environment is far too easy to set accidentally for something that controls whether destructive actions are reachable.Both accept
trueor1; anything else — including an unset variable — means off.
A tool result is capped at 32 KB (ES_MAX_RESULT_BYTES). This matters on a logging cluster: before the cap, one list_shards call over a year of daily indices returned 385 KB — approximately 96,000 tokens — in a single answer, which exceeds what most sessions can handle.
When a result is trimmed, it reports that, indicates how much was omitted, and tells you how to ask a smaller question. Three tools shape their answers around this limit:
list_indices and list_shards return a readable summary; the same rows are available as text behind verbose.
search caps size at 100 per call and tells you the from to use for paging.
get_mappings lists the fields first and the raw mapping second, so an index with a thousand fields still answers the question it was asked.
Four tools — list_indices, list_shards, get_index_settings, and get_mappings — also provide their answer as typed structured output, so a client can read the rows instead of parsing the text. It is assembled from whatever space is left after the readable answer, and reports returned against total so a partial listing can be detected by number.
Run pnpm run measure against the built output to see the current values for your own configuration.
Most setups declare this server more than once — one entry per cluster. The entries are otherwise identical, so a client shows two servers with the same name and no way to tell them apart. ES_INSTANCE_LABEL becomes the server’s display title, and it is the natural place to indicate which environment an entry targets:
{
"mcpServers": {
"es7-prod": {
"command": "npx",
"args": ["-y", "@agrica/elasticsearch7-mcp"],
"env": {
"ES_HOST": "https://es-prod:9200",
"ES_API_KEY": "prod-key",
"ES_INSTANCE_LABEL": "production",
"ES_ADMIN_TOOLS": "true"
}
},
"es7-staging": {
"command": "npx",
"args": ["-y", "@agrica/elasticsearch7-mcp"],
"env": {
"ES_HOST": "https://es-staging:9200",
"ES_API_KEY": "staging-key",
"ES_INSTANCE_LABEL": "staging",
"ES_ADMIN_TOOLS": "true",
"ES_ALLOW_DESTRUCTIVE": "true"
}
}
}
}이 구성 쌍이 의도된 형태입니다. 진단은 양쪽 모두에, 삭제는 스테이징에서만 허용합니다. 프로덕션에서는 비정상 인덱스를 설명하는 도구만 제공하고, 데이터를 제거할 수 있는 도구는 절대 노출하지 않습니다. 등록되지 않은 도구는 모델이 호출할 수 없기 때문입니다.
이 라벨은 시작 시 stderr에도 출력됩니다. 클라이언트가 연결을 보고했는데 어느 클러스터가 응답했는지 알 수 없다면 stderr를 확인하면 됩니다.
고가용성과 부하 분산을 위해 여러 Elasticsearch 노드를 구성할 수 있습니다.
{
"mcpServers": {
"elasticsearch7-mcp": {
"command": "npx",
"args": [
"-y",
"@agrica/elasticsearch7-mcp"
],
"env": {
"ES_HOST": "https://es-node1:9200,https://es-node2:9200,https://es-node3:9200",
"ES_API_KEY": "your-api-key"
}
}
}
}클라이언트는 구성된 노드 사이의 장애 조치와 부하 분산을 자동으로 처리합니다.
각 릴리스는 멀티 아키텍처 이미지(linux/amd64, linux/arm64)를 GitHub Container Registry에 게시합니다.
docker pull ghcr.io/agrica/elasticsearch7-mcp:latest서버는 stdio를 사용하므로 컨테이너에는 인터랙티브 stdin이 필요하고 공개된 포트는 없습니다. MCP 클라이언트에서는 다음과 같이 지정합니다.
{
"mcpServers": {
"elasticsearch7-mcp": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-e", "ES_HOST",
"-e", "ES_API_KEY",
"ghcr.io/agrica/elasticsearch7-mcp:latest"
],
"env": {
"ES_HOST": "your-elasticsearch-host",
"ES_API_KEY": "your-api-key"
}
}
}
}[!NOTE] npm 패키지와 마찬가지로 이미지는 GitHub Packages에 있습니다. 저장소가 공개되어 있어도 이미지를 가져오려면
read:packages범위가 있는 토큰이 필요합니다.
이미지에는 공개된 포트나 볼륨이 필요하지 않습니다. stdio로 통신하며, MCP 클라이언트가 stdin과 stdout을 소유합니다.
[!TIP] 다음은 MCP 클라이언트로 시도해 볼 수 있는 자연어 쿼리 예시입니다.
"내 Elasticsearch 클러스터의 상태는 어떤가요?"
"내 클러스터에는 활성 노드가 몇 개 있나요?"
"내 Elasticsearch 클러스터에 어떤 인덱스가 있나요?"
"'users'라는 인덱스를 샤드 3개, 복제본 1개로 생성해 주세요."
"'old_index'에서 'new_index'로 데이터를 다시 인덱싱해 주세요."
"'products' 인덱스의 필드 매핑을 보여 주세요."
"'products' 인덱스에 'tags'라는 keyword 유형 필드를 추가해 주세요."
"지난달 $500가 넘는 모든 주문을 찾아 주세요."
"어떤 제품이 5점짜리 리뷰를 가장 많이 받았나요?"
"'customers' 인덱스에 이 고객 레코드들을 일괄 가져오세요."
"'logs-*' 패턴의 로그용 인덱스 템플릿을 생성해 주세요."
"나의 인덱스 템플릿을 모두 보여 주세요."
ES_ADMIN_TOOLS=true 필요)"'logs-2026' 인덱스가 yellow 상태인데 왜 샤드가 할당되지 않았나요?"
"디스크 웟터마크에 가까운 노드가 있나요?"
"내 인덱스 중 가장 큰 것은 무엇이며, 삭제된 문서는 얼마나 되나요?"
"이 클러스터에서 누군가 샤드 할당을 잠근 것이 있나요?"
"리인덱스가 아직 실행 중인가요?"
ES_ALLOW_DESTRUCTIVE=true 필요)"'smoke-test-source' 인덱스를 삭제해 주세요."
"'logs-archive'에 있는 2024년 이전 문서를 모두 제거해 주세요."
증상 | 원인 |
설치 중 또는 | 사용자 수준 |
시작 시 |
|
클라이언트가 연결되지만 진단 또는 삭제 도구가 누락됨 | 해당 도구 세트는 게이트되어 있습니다. |
| 의도된 동작입니다. 파괴적 도구는 플래그가 켜져 있어에도 패턴이 아니라 구체적인 인덱스 이름 하나만 처리합니다. |
product check(제품 확인) 관련 연결 오류 | 클러스터가 8.x이거나 연결할 수 없습니다. 이 빌드는 7.x에서만 동작합니다. |
버그를 발견했거나 필요한데 없는 도구가 있나요? GitHub 저장소에 이슈를 열어 주세요. 코드를 수정하려면 CONTRIBUTING.md에서 시작하세요.
Show the terms a text is broken into, which is what a query must produce to match. Pass field with index to use the analyzer that index really applies to that field; that is the form that explains a search returning nothing.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze | |
| field | No | Use this field's analyzer, taken from the index mapping. | |
| index | No | Index whose analyzers to use. Required with field. | |
| analyzer | No | Named analyzer to test instead, e.g. standard, french. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, so no safety disclosure is missing. The description adds semantic behavior: output is the token terms a query must match, and using field/index routes to the real per-field analyzer. It neither contradicts annotations nor reveals side-effect caveats beyond that, so a mid score is appropriate.
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?
Two compact sentences, the first states the core function and the second adds the key usage nuance. No filler or repetition of schema details.
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?
For a read-only diagnostic with one required parameter, the description plus schema covers what the tool does, how to choose a real-field analyzer, and why it matters for debugging searches. No output schema exists, but the description directly states the observable result ('terms a text is broken into'). It doesn't discuss response shape details, but they are unnecessary for basic 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 coverage is 100%, so parameter basics are already documented. The description adds the important combination rule (field must be paired with index) and explains that the analyzer is taken from the index mapping, which is value beyond individual property docs. It earns an above-baseline score.
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 names a concrete operation — showing the tokenized terms produced from text — and ties it to query matching, so an agent understands what analyze returns. It also distinguishes the field/index form from a standalone analyzer test. This goes well beyond the vague title.
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?
It gives explicit guidance on the field-index combination as the way to use the actual analyzer applied by the index, and states this is the form that explains an empty search result. It doesn't explicitly enumerate alternatives or when to use the analyzer parameter, but the context is sufficient for a user to choose sensibly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Index up to 1000 documents in one call. The index is refreshed by default, so they are searchable immediately; per-document failures are reported without failing the call.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Target index | |
| idField | No | Field whose value becomes the document _id. Omit to let Elasticsearch generate ids. | |
| refresh | No | Defaults to true, making documents searchable at once. Set false when loading many batches: forcing a refresh per batch is the expensive part. | |
| documents | Yes | Documents to index, at most 1000. Send more in successive batches. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful behavioral detail beyond the annotations: the index is refreshed by default so documents are immediately searchable, and per-document failures are reported without failing the whole call. This meaningfully informs the agent about side effects and error handling.
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?
Two sentences deliver the key information without redundancy: the action, the batch limit, the refresh behavior, and the failure handling. It is front-loaded with the most important operational constraint.
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 a complete input schema, a moderate number of parameters, and annotations covering the read/write profile, the description covers the important behavioral nuances—immediate searchability and partial failures. No critical information for invoking the tool correctly is missing.
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 100%, so the parameters are already well documented in the schema. The description reinforces the 1000-document limit and the default refresh behavior, but adds little semantic value beyond what the schema already states.
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 a specific action ('Index'), a concrete resource ('documents'), and a clear constraint ('up to 1000 documents in one call'). This clearly separates it from siblings like create_index, create_mapping, and reindex, which target different operations.
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?
The description establishes when bulk indexing is appropriate: when you have up to 1000 documents to index in a single call. It also explains the default refresh behavior, which helps an agent decide when to override it. It does not explicitly name alternatives, but the batching context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cluster name, Elasticsearch version and build flavour. The version decides which query DSL features exist.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful context about the returned fields and the role of the version, but it does not disclose additional behavioral traits like response structure, authentication needs, or whether values are cached.
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 two short sentences: the first front-loads the exact fields returned, and the second gives a brief reason the version matters. There is no filler or redundant restating of the title.
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?
For a parameterless, read-only metadata tool without an output schema, the description adequately covers what the agent gets (name, version, flavour) and why version is useful. It could be slightly richer by noting the response is a flat JSON object, but that is a minor omission for such a simple tool.
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 tool takes zero parameters, so there is no parameter burden on the description. The schema is effectively empty and the baseline for 0 params is 4.
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 identifies the resource (cluster identity: name, version, build flavour), but it uses a noun phrase rather than a specific verb like 'returns' or 'lists'. It is distinguishable from siblings by its focus on cluster metadata, yet it never explicitly differentiates itself from elasticsearch_health or other info tools.
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?
The statement 'The version decides which query DSL features exist' implies when this tool is relevant: checking version-dependent DSL feature support. However, it does not explicitly say when to use it versus alternatives, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count the documents matching a query, without transferring any. Cheaper than search when only the number matters.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index to count in | |
| query | No | Query DSL. Omit to count every document. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond annotations: no documents are transferred and this operation is cheaper than search. This is meaningful information without contradicting the annotations.
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?
Two short sentences with no redundant words. The core action is front-loaded and the benefit/alternative is stated in the second sentence.
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?
For a simple read-only counting tool, the description, schema, and annotations together cover the required parameters, the optional query behavior, the safety profile, and the use case. The return value (a count) is obvious from the tool's purpose, so no output schema is needed.
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 100%, so the schema already documents index and query, including that omitting query counts every document. The description does not add further parameter-level meaning but does not need to.
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 uses a specific verb-resource pairing ('Count the documents matching a query') and immediately differentiates from the sibling search tool by noting it transfers no documents. An agent can tell exactly what operation this performs and how it differs from search.
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?
It explicitly states the condition for choosing count over search: 'when only the number matters,' and notes it is 'Cheaper than search.' The phrase 'without transferring any' also implies that search should be used when actual documents are needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new index. Fails if it already exists — use create_mapping to add fields to an existing one.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index name | |
| mappings | No | Field mappings, e.g. { properties: { title: { type: 'text' } } } | |
| settings | No | Index settings, e.g. number_of_shards, number_of_replicas |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds a valuable behavioral detail beyond the annotations: the operation fails rather than overwriting when the index already exists. This complements the annotations, which already indicate a non-read-only, non-idempotent write operation. It does not elaborate on all side effects, but the conflict behavior is the key operational trait.
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 two sentences with no filler. It leads with the core action, then immediately states the failure condition and the correct alternative. Every sentence serves a distinct purpose and the structure makes it easy to scan.
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?
The description, together with the fully documented schema and annotations, provides enough context for correct invocation: required parameter, optional mapping/settings parameters, and the failure mode. It does not mention the create_index_template sibling, but that is not essential for invoking this specific tool correctly.
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?
All three parameters are already documented in the input schema, including examples for mappings and settings, so schema coverage is 100%. The description does not add additional parameter-level meaning, but it does not need to; the schema carries that burden effectively.
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 a specific action and resource: 'Create a new index.' It also differentiates itself from the closest sibling, create_mapping, by explicitly saying to use that tool for adding fields to an existing index. The purpose is immediately clear and not a tautology.
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?
The description gives explicit when-to-use guidance: use this tool for creating a new index. It also gives an explicit when-not-to-use and alternative: 'Fails if it already exists — use create_mapping to add fields to an existing one.' This leaves little ambiguity about which tool to select.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create or update a composable index template (Elasticsearch 7.8+). It applies only to indices created after it, never to existing ones.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Template name | |
| version | No | Version number, for your own tracking | |
| priority | No | Precedence when several templates match; higher wins | |
| template | Yes | What to apply: { settings, mappings, aliases } | |
| indexPatterns | Yes | Index wildcards the template applies to, e.g. ['logs-*'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a mutating, idempotent, non-destructive operation. The description adds important behavior beyond the annotations: the Elasticsearch 7.8+ requirement and the non-retroactive nature of the template. No contradiction exists between description and annotations.
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?
A single sentence communicates the action, resource, version constraint, and the most important behavioral caveat with no filler. The key scoping limitation is front-loaded 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?
For a write tool with 5 parameters, a nested template object, and no output schema, the description covers the decisive behavioral constraint and version compatibility while relying on the fully documented schema for parameter details. It does not mention the response format, but that is a minor gap for a create/update tool.
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 100%, so all five parameters already have inline documentation. The description does not add substantial parameter-level meaning beyond the schema, making the baseline 3 appropriate.
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 a specific verb ('Create or update') and a specific resource ('composable index template'), which clearly distinguishes it from sibling tools like create_index or create_mapping. The version qualifier and 'applies only to indices created after it' add further precision about what the tool 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?
The description gives clear context: this tool is for defining templates that affect future indices only, and it explicitly states it never affects existing indices. It does not name alternative tools for existing-index changes, but the exclusion is clear enough for an agent to reason about when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add or update fields on an index, creating the index if it does not exist. Elasticsearch cannot change the type of an existing field.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index name | |
| mappings | Yes | Mapping to apply, e.g. { properties: { tags: { type: 'keyword' } } } |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate idempotent, non-read-only, and non-destructive behavior. The description adds valuable constraints beyond annotations, especially the critical limitation that 'Elasticsearch cannot change the type of an existing field', which helps agents anticipate failures.
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?
Two tightly written sentences. The first conveys the core action and index-creation behavior, and the second adds the most important caveat. There is no filler or repetition.
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?
For a two-parameter tool with a clear schema, the description covers the main behavioral caveat and typical usage. It does not describe the response shape, but given no output schema and the simplicity of the operation, the description is largely sufficient.
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 100%, with both parameters documented well; the mappings parameter even includes an example shape. The description itself adds no additional parameter-level detail, so the baseline of 3 is appropriate.
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 a specific verb ('Add or update') and resource ('fields on an index') and clarifies that it can create the index if needed. This clearly differentiates it from sibling tools like get_mappings or create_index.
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?
The description gives clear context: use this tool to mutate mappings, including when the index does not yet exist. It does not explicitly name alternatives or state when not to use it, but the operational scope is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cluster health: status, node counts and shard counts.
| Name | Required | Description | Default |
|---|---|---|---|
| includeIndices | No | Add per-index health detail |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not need to re-establish safety. The description does add useful information about the return content (status, node counts, shard counts), which is helpful because there is no output schema. It does not add deeper behavioral context such as whether includeIndices changes response structure substantially, but that is partially covered by the parameter description.
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 one short, front-loaded sentence: 'Cluster health: status, node counts and shard counts.' Every word earns its place, and there is no redundancy or filler.
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?
For a simple read-only tool with zero required parameters and a well-documented optional parameter, the description covers the essential return content. The annotations cover the safety profile, and the schema covers the parameter. It is only missing explicit guidance for distinguishing it from cluster_info, which is already penalized under usage guidelines.
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 100%, and the only parameter, includeIndices, already has a meaningful description: 'Add per-index health detail'. The tool description adds no additional parameter semantics beyond the schema, so the baseline of 3 applies.
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 names the exact resource ('cluster health') and the specific output areas (status, node counts, shard counts), which makes the tool's purpose reasonably clear. It lacks an explicit verb like 'get', but the noun-phrase style with tool name 'elasticsearch_health' is sufficient. It is distinguishable from index-focused siblings like list_indices and get_mappings, though cluster_info could overlap without more detail.
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?
The description gives no guidance on when to use this tool versus alternatives. With sibling cluster_info likely covering similar cluster-level information, the absence of a routing note is a real gap. Any usage guidance is only implied by the name and the word 'health'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Which fields exist across an index pattern and whether each is searchable and aggregatable. Takes a wildcard, unlike get_mappings, and is the only way to see a field mapped as two different types across indices — which makes an aggregation over it silently partial rather than failing.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index, alias or wildcard, e.g. logs-app-* | |
| fields | No | Field name or wildcard to ask about, e.g. log.* — comma-separated for several. Defaults to every field. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: wildcard acceptance, cross-index type conflict visibility, and the non-obvious consequence that aggregations over such fields become silently partial rather than failing. This is exactly the kind of behavioral warning an agent needs.
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?
Two tight sentences: the first delivers the core purpose, the second adds the distinguishing behavior and its operational warning. Every clause earns its place, and the most decision-relevant information is front-loaded.
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?
For a simple two-parameter, read-only tool, the description covers the essential facts: what it returns, how it differs from its sibling, and a critical edge-case consequence. The schema and annotations carry the remaining details, so nothing an agent needs to invoke it correctly is missing.
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 input schema already describes both parameters clearly, including wildcard behavior and defaults, giving 100% schema description coverage. The description does not substantially add parameter-level meaning beyond that, so the baseline of 3 is appropriate; it reinforces the wildcard theme but introduces no new parameter detail.
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 opens with a precise statement of what the tool returns: fields across an index pattern plus whether each is searchable and aggregatable. It further distinguishes itself from get_mappings by calling out wildcard support and the special case of fields mapped as two different types, making the tool's identity unmistakable.
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?
The description explicitly names get_mappings as the alternative and gives a concrete differentiator ('Takes a wildcard, unlike get_mappings'). It also establishes a strong when-to-use condition: it is the only way to detect a field mapped as two different types. It does not spell out the reverse case ('use get_mappings when...'), but the intended routing is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List which aliases point to which indices. A name you query may be an alias, which changes what get_mappings returns.
| Name | Required | Description | Default |
|---|---|---|---|
| index | No | Index or wildcard. Omit for the whole cluster. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds behavioral context by warning that aliases affect get_mappings results, which is useful beyond the annotations. No contradiction exists.
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?
Two sentences, no filler. The core purpose is front-loaded, and the second sentence adds valuable contextual information without unnecessary length.
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?
For a simple read-only tool with one optional parameter and no output schema, the description provides sufficient guidance. It could mention what the response contains, but the annotations and sibling context cover most operational concerns.
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 100%, so the optional 'index' parameter is already documented. The description doesn't add parameter-level detail beyond what the schema states, which matches the baseline for high schema coverage.
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 uses a specific verb and resource: 'List which aliases point to which indices.' It clearly communicates the tool's purpose and implicitly distinguishes it from sibling tools like list_indices and get_mappings.
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?
The description gives concrete context for when this tool matters: when a queried name may be an alias and alters what get_mappings returns. It does not explicitly name alternatives or exclusion criteria, but the usage context is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one document by its _id. Reports that it is absent rather than failing.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Document _id | |
| index | Yes | Index holding the document |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds a meaningful behavioral trait beyond those annotations: it reports that a document is absent rather than failing. This tells the agent how missing documents are handled, which is useful for interpreting results.
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 two short sentences with no wasted words. The core action is front-loaded, and the second sentence adds a useful behavioral note. It is concise without sacrificing important information.
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?
For a simple get-by-id tool with two fully documented parameters and strong annotations, the description is largely complete. It does not specify the exact return shape or concrete absent representation, such as null or an empty result, but 'Fetch one document' and 'Reports that it is absent rather than failing' cover the essential behavior an agent needs.
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 100%, so both parameters are already documented in the schema. The description adds little beyond the schema, aside from referring to '_id' in a way that maps to the id parameter. Baseline 3 is appropriate because the schema carries the parameter semantics.
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 a specific verb and resource: 'Fetch one document by its _id.' It clearly distinguishes this from sibling tools like search or get_index_template by narrowing scope to a single document identified by _id. The additional detail about reporting absence rather than failing reinforces its unique purpose.
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?
The description gives a clear usage context: use this tool when you need exactly one document by its _id. It does not explicitly name alternatives or exclusions, but the stated single-document-by-id behavior is enough to imply the appropriate scenario and separate it from broader search or listing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get composable index templates. Omit name to list all.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Template name. Omit to list every template. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety semantics. The description adds the list-all behavior when name is omitted, but does not disclose output shape, errors, or other runtime 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 two short, purposeful sentences with no filler. The main operation is front-loaded, and the optional-name behavior is expressed efficiently.
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?
For a simple, optional-parameter, read-only tool with strong annotations, the description is nearly complete. It could mention the return format or that it returns template definitions, but an agent has enough information to invoke it correctly.
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 100%, and the schema already explains that 'name' is optional and that omitting it lists every template. The description restates this without adding new semantic details, so the baseline score of 3 is appropriate.
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 operation ('Get'), the resource ('composable index templates'), and the key behavior ('Omit name to list all'). This makes the tool's purpose immediately obvious and distinguishes it from create_index_template and other read-only sibling tools.
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?
The description gives clear guidance on omitting the name to list all templates, but it does not explicitly discuss when to choose this tool over alternatives such as get_mappings or create_index_template. Usage context is mostly implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the fields of one index as dotted paths with their types, nested fields included, then the raw mapping. Give a concrete index name: an alias or a wildcard returns nothing.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index name |
| Name | Required | Description |
|---|---|---|
| found | Yes | |
| index | Yes | |
| total | Yes | |
| fields | Yes | |
| omitted | Yes | |
| returned | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavior beyond annotations by specifying the output format (dotted paths with types, then raw mapping) and the alias/wildcard caveat, which is exactly the kind of context an agent needs.
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?
Two compact sentences front-load the tool's output behavior and then give the critical input constraint. Every sentence earns its place, with no filler or repetition of the schema.
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 simple one-parameter schema, the presence of an output schema, and annotations covering safety, the description is complete. It explains what the response contains, when a call will succeed, and when it will return nothing.
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 fully documents the single 'index' parameter with 100% coverage, so the baseline is 3. The description adds real semantic value by requiring a concrete index and warning that aliases and wildcards yield no results, which is not inferable from the schema alone.
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 a specific action ('List the fields of one index as dotted paths with their types') and a concrete resource ('one index'), and it distinguishes the tool by noting nested fields are included and the raw mapping follows. This separates get_mappings from siblings like get_index_template or field_caps without requiring the agent to inspect schemas.
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?
The description gives clear operational guidance: pass a concrete index name, and avoid aliases or wildcards because they return nothing. It does not explicitly name alternative sibling tools, but the precondition and failure mode are actionable enough for an agent to decide when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check an asynchronous task, such as the one reindex returns: progress, completion and failure.
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | Task id as returned by reindex, e.g. node-1:428 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds that the task reports progress, completion, and failure, but it does not explain response format or polling behavior; this is modest added value beyond the annotations.
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 front-loaded sentence that names the action, the resource, the source of the task id, and the relevant outcomes. Every phrase earns its place with no redundant wording.
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?
For a single-parameter polling tool with rich annotations, the description is complete: it says what to check, where the task id comes from, and what aspects are reported. No output schema exists, but the description names the key result dimensions well enough for correct 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?
The schema already provides 100% coverage for taskId, including its meaning and an example. The description only reinforces that the task id comes from reindex, adding no new parameter semantics beyond what the schema already supplies.
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 tool checks an asynchronous task and identifies the specific scenario (tasks returned by reindex). It names the observable outcomes (progress, completion, failure), making the purpose unambiguous, though it does not explicitly differentiate from siblings.
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?
The description provides a clear usage context: use this after an asynchronous operation such as reindex that returns a task id. It does not state when-not-to-use or name alternatives, but the context is concrete enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List indices, one compact line each: name, health, status, document count and size in bytes. Large results are trimmed and say so.
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | No | Elasticsearch wildcard, e.g. `logs-*`. Not a regex. Defaults to `*`. | |
| verbose | No | Also repeat the rows as JSON text, for a client that does not read structured output. |
| Name | Required | Description |
|---|---|---|
| total | Yes | |
| indices | Yes | |
| omitted | Yes | |
| pattern | Yes | |
| returned | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the read-only and idempotent annotations by disclosing the compact per-line format and the important trimming behavior for large results. This prevents an agent from assuming the output is always complete. Safety is already covered by annotations.
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?
Two short sentences front-load the purpose and output shape, then add the trimming caveat. Every phrase earns its place and there is no boilerplate.
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?
With only two optional parameters, a full output schema, and annotations covering safety and idempotence, the description supplies the key behavioral caveat (trimming) and output fields. An agent has everything needed to invoke list_indices correctly.
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 input schema already documents both parameters at 100% coverage, including the wildcard-vs-regex distinction for pattern and the purpose of verbose. The description adds no parameter-level detail, so the baseline score of 3 is appropriate.
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 a specific operation ('List indices') and defines the exact output fields (name, health, status, document count, size in bytes), making it immediately distinguishable from sibling tools like get_mappings or field_caps. It clearly identifies the resource and the compact representation.
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?
The output description clearly implies its use case: obtaining a compact overview of indices with health and size information. It does not explicitly name sibling alternatives or state exclusions, so it falls just short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Copy documents between indices. Asynchronous: returns a task id at once and the copy is still running on return — poll it with GET _tasks/.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Query DSL selecting which documents to copy. Omit to copy all. | |
| script | No | Transform each document while copying | |
| destIndex | Yes | Destination index, created if absent | |
| sourceIndex | Yes | Source index |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by disclosing the crucial asynchronous behavior: it returns a task id immediately while the copy continues, and it tells the caller how to poll progress. Annotations already cover read/write/destructive aspects, and the description adds meaningful operational context without contradicting them.
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?
Two tight sentences: the first states the action clearly, and the second adds the key behavioral caveat and how to follow up. No filler or repetition of schema content.
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?
For a tool with nested parameters and no output schema, the description explains the main return signal (task id) and the continuation mechanism. It is sufficient for an agent to select and invoke the tool correctly, though it does not detail edge cases or result formats beyond the task id.
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 100%, and the schema already documents sourceIndex, destIndex, query, and script with meaningful descriptions. The tool description adds no parameter-specific detail, but the schema carries that burden fully, so the baseline 3 is appropriate.
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 uses a specific verb and resource pair: 'Copy documents between indices.' This clearly distinguishes reindex from read-only search, single-document get, and indexing/bulk operations, and the title reinforces the same intent.
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?
The description clearly implies when to use this tool: when documents must be copied between indices. However, it does not explicitly explain when not to use it or mention alternatives such as bulk for direct writes or search for read-only queries. The async polling hint gives practical usage context, but no explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search one index with a query DSL body. Matching text fields are highlighted. Paging goes inside queryBody (size, from); size is capped at 100 per call, so page with from for more.
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | Index to search | |
| queryBody | Yes | Query DSL body: query, size, from, sort, aggs, _source. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety signals (readOnly, idempotent, non-destructive). The description adds valuable behavioral details beyond those: highlighting behavior and the hard cap on `size`. This is meaningful operational context that the annotations do not cover.
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?
Three sentences, each contributing necessary information: scope, highlighting behavior, and paging constraints. The most important usage constraint is front-loaded. No filler or redundancy.
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?
The description sufficiently covers scope, input, highlighting, and paging constraints. There is no output schema, and the description does not detail the response structure, but the core behavior and critical operational details are present. Slight gap around return format, but not severe for a search tool.
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 coverage is 100%, so the schema documents both parameters well. The description adds extra semantic value by explaining how paging works inside queryBody and the size limit, which is not obvious from the schema alone.
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 a specific action ('Search one index') and resource ('one index') with a query DSL body, clearly distinguishing it from sibling tools like count, get_document, and list_indices. The scope is precise and immediately understandable.
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?
The description gives concrete usage guidance on paging, including the size cap of 100 and how to use `from` for more results. It does not explicitly compare to alternatives like count or get_document, but the 'one index' scoping and query DSL focus provide clear context.
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.
v0.4.1analyzebulkcluster_infocountcreate_indexcreate_index_templatecreate_mappingelasticsearch_healthfield_capsget_aliasesget_documentget_index_templateget_mappingsget_tasklist_indicesreindexsearchScored across 17 tools
Each tool targets a distinct operation or resource: field inspection is split cleanly between get_mappings and field_caps, with explicit notes about wildcard vs concrete index behavior. create_index, create_mapping, bulk, and reindex are also clearly differentiated, so an agent should not confuse them.
Most tools use get_, list_, or create_ prefixes, but bare nouns and verbs like bulk, count, reindex, field_caps, cluster_info, and elasticsearch_health break the verb_noun pattern. Naming is readable and consistently snake_case, but the conventions are mixed.
17 tools is on the heavy side for a single server and makes agent tool selection slightly harder. Each tool does map to a real Elasticsearch operation, so the count is not excessive, but it is borderline.
The surface covers health, mappings, search, indexing, and templates, but lacks delete/update operations for documents and indices, and has no deletion for aliases or templates. This creates significant dead ends for common lifecycle workflows.
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