elasticsearch7-mcp
Elasticsearch 7.x MCP Server
MCP-Server, der eine direkte Verbindung von jedem MCP-Client (wie Claude Desktop, Cursor) zu deinem Elasticsearch-Cluster herstellt.
[!IMPORTANT] Dieser Fork ist ausschließlich auf Elasticsearch 7.x ausgelegt. Er legt den
@elastic/elasticsearchClient in der Version 7.17 fest, dessen Produktprüfung Server älter als 7.14 akzptiert. Für einen Elasticsearch-8.x-Cluster verwende das übergeordnete Projekt @awesome-ai/elasticsearch-mcp, von dem deser Fork abgeleitet ist – der 8.x-Client kann nicht mit einem 7.x-Server sprechen und umgekehrt.
Deser Server verbindet Agents über dass Model Context Protocol mit deinen Elasticsearch-Daten. Du kannst auf diese Weise über of einen natürliche Sprache mit deinen Elasticsearch-Indizes interagieren.
Funktionsübersicht
Die Tools bestehen aus drrei Gruppen. Die erste Group ist imer immer verfügbar; die anderen beiden werden über eine Umgebungsvariable aktivert, so dass eine Produktions-Bereitselung Diagnoswerkeuge anbietet, ohne Löschvorgänge zu exponieren. Die Freischaltung erfolgt bei der Registierung: Ein depaktiviertes Tool erscheint nie in tools.ist, sodass das Modell es nicht aufrufen kann und es den Kontext des Agents nichts kostet.
Immer verfügbar – Daten lesen und schreiben
Cluster
elasticsearch_health: Cluster-Gesundheit, optional bis auf sie auf Index-Ebenecluster_info: Clustername, Elasticsearch-Version und Build-Variante
Index-Operationen
list_indices: Indizes auflisten, gefiltert per Elasticsearch-Wildcard (log-*)create_index: Einen Index mit optionalen Einstellungen und Mappings erstellenreindex: Einen Index kopieren, optional gefiltert per Abfrage oder ejene Daten transformiert per Skriptget_aliases: Auflistung, welche Aliase auf welche Indizes zeigen
Mappings
get_mappings: Die Felder eines Index als Punktpfade samt Typen, dann das rohe Mappingcreate_mapping: Mapping eines Index erstellen oder aktualisieren
Suche und Daten
search: Eine Query-DSL-Suche ausführen, mit hervorgehobem Text über jedes Textfelder – einschließlich verschachtelter – außerdem die Abfrage kein eigeneshighlightmitbringtcount: Anzahl übereinstimmender Dokumente ermitteln, ohne sie zu übertragenget_document: Ein Dokument per ID abrufenbulk: Viele Dokumente gleichzeitig indizieren
Vorlagen (Templates)
create_index_template: Eine zusammensetzbare Index-Vorlage erstellen oder aktualisierenget_index_template: Index-Vorlagen lesen
Aufgaben (Tasks)
get_task: Fortschritt einer langlaufenden Aufgabe, wie sie zum Beispielreindexzurückgibt
ES_ADMIN_TOOLS=true – Diagnose (nur lesend)
Diese Tools_können nur gelesen weden, ist in Prodktionen unbedenklich – genau das ist der Sinn dieser Group: Ein Agent kann so erklären, warum ein Index nicht gesund ist, ohne dass jemand sich am Cluster anmeldet.
explain_allocation: Erklärt, warum ein Shard nicht zu ein Index gehört, mit the Entscheidung jedes Zuweiserslist_shards: Status auf Shard-Ebene, zuerst der Kopien, die nichtSTARTEDsindget_nodes: Speicher, CPU, Last und Plattenauslastung pro Nodeget_index_stats: Zähler pro Index – Größe, Segmente, Indize, Suchen, Mergesget_index_settings: Einstellungen eines Index (refresh_interval, Replicas, Schreibschutz-Blöcke)get_cluster_settings: Cluster-Einstellungen, die zur Laufzeit geändert wurdenlist_tasks: Was der Cluster gerade ausführt
ES_ALLOW_DESTRUCTIVE=true – nicht umkehrbar
Gedacht für eine Staging-Umgebung und standardmäßig deaktiviert, sodass Produktionssyststeme sie gar nicht erst erreichen können.
delete_index: Löscht einen Index und dessen Datendelete_document: Löscht ein Dokument anhand der IDdelete_by_query: Löscht alle Dokumente, die auf einen Query passen – asynchron, die einen Task-ID zurückbekommt und die Löschung im Hintergrund fortgesetzt wirddelete_index_template: Löscht eine Index-Vorlage
Selbst ob die Flag aktiv ist, werden Wildcards, eine kommagetrennte Liste, * und _all abgelehnt: Sie wirken immer nur auf einen bestimmten Index. Ein Modell, das logs-* für einen einzelnen Index hält, bekommt eine Ablehnung statt eines geleerten Clusters.
So funktioniert's
Der MCP-Client analisiert die Anfrag und entscheidet, welche Elasticsearch-Operationen nötig sind.
Der MCP-Server führt diese Operationen aus (Indizes auflisten, Mappings abrufen, Suchen).
Der MCP-Client verarbeitet die Ergebnisse and präsentiert sie in ansprechender Form.
Related MCP server: Elasticsearch 7.x MCP Server
Erste Schritte
Voraussetzungen
Eine Elasticsearch-7.x-Instanz (getestet gegen 7.8; der 7.17-Client unterstützt 6.8 bis 7.x)
Elasticsearch-Zugangsdaten – ein API-Key oder ein Benutzername mit Passwort
Ein MCP-Client: Claude Code, Claude Desktop, Codex, Cursor oder alles andere, das MCP über stdio spricht
Einmalige Anmeldung bei GitHub Packages
[!IMPORTANT] Dieses Paket wird in GitHub Packages veröffentlicht, nicht auf npmjs.com. GitHub Packages verlangt auch für öffentliche Pakete einen Token; bis du sie hinzufügst, schlägt jede Installation unten einem 401-Fehler. auf. Platz der Token in deiner benutzerbezogenen
~/.npmrc:@agrica:registry=https://npm.pkg.github.com //npm.pkg.github.com/:_authToken=YOUR_GITHUB_TOKEN
YOUR_GITHUB_TOKENist ein Personaltokale mit dem Umfangread:packages.Lass es and der eigenen
~/.npmrc, nicht in einer Projekt file liegen. Ein Token, der public Commit é una Kommittierung eines Repositoriums ist ein verlorener Tokenet, und einige Paketmanager weigern sich, ihn überhaupt darüber zu lesen.
Verbinde es mit deinem Client
Jedes Beispiel unten setzt ES_HOST und ES_API_KEY. Du kannst ES_USERNAME/ES_PASSWORD für Basic Auth verwenden, ES_ADMIN_TOOLS=true hinzufügen, um die Diagnose Tools zu erhalten, und ES_INSTANCE_LABEL setz, wenn mehr der eleme enter mehrere Instanzen definiert – siehe 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-mcpDanach liste /mcp in der Sitzung den Server und die Tools.
Zwei Macken, die leicht falsch laufen können:
Alles ein nach
--ist der Befehl, der den Server erstartet; ohne das würde Claude Code-yals ein eigenes Flagge probieren.Leg den Server Namen nicht direkt vor
--env– die CLI list ihn auf alsKEY=value-Paar und lehnt ihn ab. Oben steht der Name zum Zuerst, deshalb funktioniert es.
Der Serverwird im lokalen Scope hinzugefügt, also nur in diesem Projekt. Trage --scope user ein, um global verfügbar, oder --scope project um es in .mcp.json zu schreiben und mit Team zu teilen – beachte, dass ein eingechecktes .mcp.json deinen API-Key enthält, deshalb für Zugangsdaten lieber user scope.
Ändere claude_desktop_config.json – Settings > Developer > Edit Config öffnet sie, oder finde die Datei unter %APPDATA%\Claude\ auf Windows und unter ~/Library/Application Support/Claude/ auf 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"
}
}
}
}Starte Claude Desktop dann neu; es liest diese Datei nur beim Start.
codex mcp add elasticsearch7 \
--env ES_HOST=https://your-cluster:9200 \
--env ES_API_KEY=your-api-key \
-- npx -y @agrica/elasticsearch7-mcpOder schreibe es per Hand in ~/.codex/config.toml. Hinweis: Deser Codex schreibt den Abschnitt mcp_servers mit Unterstrich, und die Umgebungsvariablen stehen in einer eigenen Unterstruktur statt 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 innerhalb von Codex bestätige, dass der Server geladen ist.
Der Server ist ein normaler stdio-MCP-Server, also funktioniert alles in der Rubrik Liste der MCP-Clients. Es sind drei Dinge nötig: Befehl npx, Argumente -y @agrica/elasticsearch7-mcp und die ES_*-Variablen in seiner Umgebung. Deser Server hört nie on einen Port und schreibt nichts außer dem MCP-Protokoll nach stdout – Diagnostik goes to stderr.
Konfiguration
Der Elasticsearch MCP Server unterstützt Einstellungen für die Verbindung zu Elasticsearch:
[!NOTE] Du musst n einen API-Schlüssel oder beides, Benutzername und Passwort für Auth angeben.
Environment Variable | Beschreibung | Erforderlich | |
| URL(s) deiner Elasticsearch-Instanz – unterstützt eine URL oder mehrere, durch Komma getrennt (unterstützt auch | Ja | |
| Elasticsearch-API-Schlüssel für die Authentifizierung (auch Legacy | Nein | |
| Elasticsearch-Benutzname für die Basisauthentifizierung (auch Legacy | Nein | |
| Elasticsearch-Passwort für die Basisauthentifizierung (auch Legacy | Nein | |
| Pfad zum CA-Zertifikat für Elasticsearch SSL/TLS (auch Legacy | Nein | |
| Zeitüberschreitung pro Anfrage in Millisekunden. Standard | Nein | |
| Anzahl Versuch pro Anfrage. Standard | Nein | |
| Obergrenze für ein Tool-Ergebnis. Standard | Nein | |
| Frei eingbarer Name dieser Bereitstellung, z.B. | Ja? Nein? | ? Nein |
|
| Nein | |
|
| Nein |
[!WARNING]
ES_ADMIN_TOOLSandES_ALLOW_DESTRUCTIVEhaben keine Legacy-Alien ohne-Präfix, im Gegensatz zu den Verbindungsvariablen oben. Das ist Absicht: Wenn du eine variableADMIN_TOOLSoderALLOW_DESTRUCTIVEin der Umgebung hast, ist viel zu leicht unwillkürlich setzen, wenn du dabei löschbarsten.Beide akzeptieren
trueoder1; alles andere, einschließlich einer nicht gesetzten Variable, bedeutet aus.
Ergebnis-größe
Ein Tool-Ergebnis ist auf 32 KB (ES_MAX_RESULT_BYTES) begrenzt. Wichtig bei einem Logging-Cluster: Vonehoben per ein einzelnes list_shards über ein Jahr tagesindexe gab 385 KB – rund 96 000 Tokens – in einer einzigen Antwort, mehr als dies meisten Sitzungen halten können.
Wenn Ergebnis gekürzt wird, sagt es das, sagt wie viel es geht, und sagt, wie man die kleinere Frage stellen kann. Drei Tools richten ihre Antworten darauf aus:
list_indicindizesundlist_shardsliefern eine einfach zu lesbare Antwort; die Zeilen als Text liegen inverbose.searchbegrenztsizeauf 100 pro Aufruf und nennt diefrom-Seitenzahl.get_mappingsbrings zuerst die Felder, dann das rohe Mapping, so dass ein Index mit tausend Feldern noch die Antwort gibt.
Vier Tools – list_indices, list_shards, get_index_settings und get_mappings – liefern ihre Ausgabe auch als typisierte structurierte Ausgabe, so dass ein Client die Zeilen lesen statt den Text zu parsen. Diese Ausgabeseite wird aus dem Platz der lesbaren Antwort zusammengesetzt und meldet returned gegenüber total, damit eine Teil-Liste als Nummer sehenbar ist.
Run pnpm run measure führe gegen die build-Ausgabe aus, um aktuelle Werte für eigene Konfiguration zu sehen.
Mehrere Instanzen kennzeichnen
Zudem packaging
In den meisten Umgebungen ist Server er mehrere Instanzen. Der Eintrag pro Cluster ist sonst identisch, ein Client zeigt für zwei Server den gleichen. Namen an, und nichts unterscheidet. ES_INSTANCE_LABEL wird zum Titel des Servers; es ist die natürliche Stelle, um zu sagen, welche Umgebung ein Eintrag verbindet:
{
"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"
}
}
}
}Das Paar ist die geplante Form: Diagnosewerkzeuge auf beiden Seiten, Löschfunktionen nur auf dem Staging-System. Die Produktion behält die Werkzeuge, die einen kranken Index erklären, und legt niemals ein Werkzeug frei, das Daten entfernen kann – das Modell kann das nicht aufrufen, was nie registriert wurde.
Das Label wird beim Start ebenfalls auf stderr ausgegeben. Dort sehen Sie nach, wenn ein Client eine Verbindung meldet, aber nicht zu erkennen ist, welcher Cluster geantwortet hat.
Konfiguration mehrerer URLs
Sie können mehrere Elasticsearch-Knoten für Hochverfügbarkeit und Lastverteilung konfigurieren:
{
"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"
}
}
}
}Der Client übernimmt automatisch Failover und Lastverteilung zwischen den konfigurierten Knoten.
Ausführen mit Docker
Jedes Release veröffentlicht ein Multi-Arch-Image (linux/amd64, linux/arm64) in die GitHub Container Registry:
docker pull ghcr.io/agrica/elasticsearch7-mcp:latestDer Server kommuniziert über stdio, daher benötigt der Container ein interaktives stdin und keinen veröffentlichten Port. In einem MCP-Client:
{
"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] Wie das npm-Paket befindet sich das Image in GitHub Packages: Das Abrufen erfordert ein Token mit dem Bereich
read:packages, auch wenn das Repository öffentlich ist.
Das Image braucht keinen veröffentlichten Port und kein Volume: Es kommuniziert über stdio; der MCP-Client verwaltet dessen stdin und stdout.
Beispielabfragen
[!TIP] Hier sind einige natürlichsprachige Abfragen, die Sie mit Ihrem MCP-Client ausprobieren können.
Cluster-Verwaltung
„Wie ist der Gesundheitsstatus meines Elasticsearch-Clusters?"
„Wie viele aktive Knoten sind in meinem Cluster?"
Indexoperationen
„Welche Indizes habe ich in meinem Elasticsearch-Cluster?"
„Erstellen Sie einen neuen Index namens 'users' mit 3 Shards und 1 Replika."
„Führen Sie einen Reindex von 'old_index' nach 'new_index' durch."
Mapping-Verwaltung
„Zeigen Sie mir die Feldzuordnungen für den 'products' Indizes."
„Fügen Sie dem Index 'products' ein Feld vom Typ 'keyword' mit dem Namen 'tags' hinzu."
Such- und Datenbetriebe
„Finden Sie alle Bestellungen über 500 $ aus dem letzten Monat."
„Welche Produkte haben die meisten 5-Sterne-Bewertungen erhalten?"
„Importieren Sie diese Kundendatensätze per Bulk in den Index 'customers'."
Vorlagenverwaltung
„Erstellen Sie eine Indizesvorlage für Logs mit dem Muster 'logs-*'."
„Zeigen Sie mir alle meine Indexvorlagen."
Diagnosefunktionen (benötigt ES_ADMIN_TOOLS=true)
„Der Index 'logs-2024' ist gelb – warum sind seine Shards nicht zugewiesen?"
„Ist ein Knoten in der Nähe eines Disk-Watermarks?"
„Welcher ist der größte Indizes, und wie viel davon sind gelöschte Dokumente?"
„Hat jemand die Shard-Zuweisung in diesem Cluster deaktiviert?"
„Läuft noch ein Reindex?"
Destruktive Operationen (benötigt ES_ALLOW_DESTRUCTIVE=true)
„Löschen Sie diesen 'smoke-test-source'-Index."
„Entfernen aus dem Index 'logs-archive' alle Dokumente, die älter als 2024 sind."
Problembehandlung
Symptom | Ursache |
| Kein GitHub-Packages-Token in Ihre |
„Serverfehler: ... ungültige URL“ beim Start |
|
Der Client verbindet sich, es fehlt aber als Diagnose- oder Löschwerkzeug. | Die Werkzeuggruppe ist geschützt. Setzen Sie |
„Verweigere Aktion auf das Muster „logs-*"" | Gewolltes Verhalten: Löschwerkzeuge akzeptieren einen einzelnen Indexnamen, nie ein Muster – auch wenn das Flag aktiviert ist. |
Ein Verbindungsfehler mit Bezug auf die Produktprüfung | Der Cluster ist 8.x oder nicht erreichbar. Diese Version + nur mit 7.x. |
Einen Fehler gefunden oder fehlt Ihnen ein Werkzeug? Erstellen Sie ein Issue im GitHub-Repository. Wenn Sie am Code arbeiten möchten, beginnen Sie mit CONTRIBUTING.md.
Available Tools
17 toolsanalyzeAnalyze textARead-onlyIdempotent
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. |
TDQS
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.
bulkBulk index documentsA
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. |
TDQS
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_infoCluster version and identityARead-onlyIdempotent
Cluster name, Elasticsearch version and build flavour. The version decides which query DSL features exist.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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.
countCount documentsARead-onlyIdempotent
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. |
TDQS
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_indexCreate an indexA
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 |
TDQS
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_index_templateCreate or update an index templateAIdempotent
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-*'] |
TDQS
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.
create_mappingCreate or update a mappingAIdempotent
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' } } } |
TDQS
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.
elasticsearch_healthCluster healthBRead-onlyIdempotent
Cluster health: status, node counts and shard counts.
| Name | Required | Description | Default |
|---|---|---|---|
| includeIndices | No | Add per-index health detail |
TDQS
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.
field_capsField capabilitiesARead-onlyIdempotent
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. |
TDQS
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.
get_aliasesList aliasesARead-onlyIdempotent
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. |
TDQS
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.
get_documentGet a document by idARead-onlyIdempotent
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 |
TDQS
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_index_templateGet index templatesARead-onlyIdempotent
Get composable index templates. Omit name to list all.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Template name. Omit to list every template. |
TDQS
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.
get_mappingsGet index mappingsARead-onlyIdempotent
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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| found | Yes | |
| index | Yes | |
| total | Yes | |
| fields | Yes | |
| omitted | Yes | |
| returned | Yes |
TDQS
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.
get_taskGet task progressARead-onlyIdempotent
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 |
TDQS
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_indicesList indicesARead-onlyIdempotent
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. |
Output Schema
| Name | Required | Description |
|---|---|---|
| total | Yes | |
| indices | Yes | |
| omitted | Yes | |
| pattern | Yes | |
| returned | Yes |
TDQS
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.
reindexReindex into another indexA
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 |
TDQS
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.
searchSearch documentsARead-onlyIdempotent
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. |
TDQS
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
17 tool updates
v0.4.1- First observed
analyze - First observed
bulk - First observed
cluster_info - First observed
count - First observed
create_index - First observed
create_index_template - First observed
create_mapping - First observed
elasticsearch_health - First observed
field_caps - First observed
get_aliases - First observed
get_document - First observed
get_index_template - First observed
get_mappings - First observed
get_task - First observed
list_indices - First observed
reindex - First observed
search
TDQS
Scored 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.
Maintenance
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Agent-driven search: build, import, tune, search, and score result quality — all over MCP.
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