Ein FastMCP-basiertes Tool für die Interaktion mit Splunk Enterprise/Cloud über natürliche Sprache. Dieses Tool bietet eine Reihe von Funktionen zum Durchsuchen von Splunk-Daten, Verwalten von KV-Speichern und Zugriff auf Splunk-Ressourcen über eine intuitive Benutzeroberfläche.
Das Tool arbeitet in drei Modi:
SSE-Modus (Standard)
Server-Sent Events-basierte Kommunikation
Bidirektionale Interaktion in Echtzeit
Geeignet für webbasierte MCP-Clients
Standardmodus, wenn keine Argumente angegeben sind
Zugriff über /sse -Endpunkt
API-Modus
RESTful-API-Endpunkte
Zugriff über das Endpunktpräfix /api/v1
Beginnen Sie mit python splunk_mcp.py api
STDIO-Modus
Standardmäßige Eingabe-/Ausgabe-basierte Kommunikation
Kompatibel mit Claude Desktop und anderen MCP-Clients
Ideal für die direkte Integration mit KI-Assistenten
Beginnen Sie mit python splunk_mcp.py stdio
Related MCP server: MCP Server for Splunk
Splunk-Suche : Führen Sie Splunk-Suchen mit Abfragen in natürlicher Sprache aus
Indexverwaltung : Splunk-Indizes auflisten und prüfen
Benutzerverwaltung : Splunk-Benutzer anzeigen und verwalten
KV Store Operations : Erstellen, Auflisten und Verwalten von KV Store-Sammlungen
Asynchrone Unterstützung : Erstellt mit Asynchron-/Wartemustern für bessere Leistung
Detaillierte Protokollierung : Umfassende Protokollierung mit Emoji-Indikatoren für bessere Sichtbarkeit
SSL-Konfiguration : Flexible SSL-Verifizierungsoptionen für unterschiedliche Sicherheitsanforderungen
Verbessertes Debugging : Detaillierte Verbindungs- und Fehlerprotokollierung zur Fehlerbehebung
Umfassende Tests : Unit-Tests, die alle wichtigen Funktionen abdecken
Fehlerbehandlung : Robuste Fehlerbehandlung mit entsprechenden Statuscodes
SSE-Konformität : Vollständig konform mit der MCP SSE-Spezifikation
Über die MCP-Schnittstelle stehen folgende Tools zur Verfügung:
list_tools
Listet alle verfügbaren MCP-Tools mit ihren Beschreibungen und Parametern auf
Gesundheitscheck
Gibt eine Liste der verfügbaren Splunk-Apps zurück, um die Konnektivität zu überprüfen
Klingeln
Einfacher Ping-Endpunkt zur Überprüfung, ob der MCP-Server aktiv ist
aktueller_Benutzer
Gibt Informationen über den aktuell authentifizierten Benutzer zurück
Benutzerliste
Gibt eine Liste aller Benutzer und ihrer Rollen zurück
Listenindizes
Gibt eine Liste aller zugänglichen Splunk-Indizes zurück
get_index_info
Gibt detaillierte Informationen zu einem bestimmten Index zurück
Parameter: index_name (Zeichenfolge)
Indizes und Quelltypen
Gibt eine umfassende Liste der Indizes und ihrer Quelltypen zurück
Suche_splunk
Führt eine Splunk-Suchabfrage aus
Parameter:
search_query (Zeichenfolge): Splunk-Suchzeichenfolge
earliest_time (Zeichenfolge, optional): Startzeit für das Suchfenster
latest_time (Zeichenfolge, optional): Endzeit für das Suchfenster
max_results (Ganzzahl, optional): Maximale Anzahl der zurückzugebenden Ergebnisse
Liste gespeicherter Suchen
Gibt eine Liste der gespeicherten Suchen in der Splunk-Instanz zurück
Liste_kvstore_sammlungen
Listet alle KV-Store-Kollektionen auf
Erstellen Sie eine KVStore-Sammlung
Erstellt eine neue KV-Store-Kollektion
Parameter: collection_name (Zeichenfolge)
delete_kvstore_collection
Löscht eine vorhandene KV-Store-Sammlung
Parameter: collection_name (Zeichenfolge)
Beim Ausführen im SSE-Modus sind die folgenden Endpunkte verfügbar:
/sse : Gibt SSE-Verbindungsinformationen im Text-/Ereignisstromformat zurück
Stellt Metadaten zur SSE-Verbindung bereit
Enthält die URL für den Nachrichtenendpunkt
Bietet Protokoll- und Fähigkeitsinformationen
/sse/messages : Der Hauptendpunkt des SSE-Streams
Streamt Systemereignisse wie Heartbeats
Hält die Verbindung dauerhaft aufrecht
Sendet ordnungsgemäß formatierte SSE-Ereignisse
/sse/health : Integritätsprüfungsendpunkt für den SSE-Modus
Gibt Status- und Versionsinformationen im SSE-Format zurück
Die MCP-Implementierung beinhaltet eine konsistente Fehlerbehandlung:
Ungültige Suchbefehle oder fehlerhafte Anfragen
Unzureichende Berechtigungen
Ressource nicht gefunden
Validierung ungültiger Eingaben
Unerwartete Serverfehler
Verbindungsprobleme mit dem Splunk-Server
Alle Fehlerantworten enthalten eine detaillierte Meldung, die den Fehler erklärt.
Python 3.10 oder höher
Poesie für das Abhängigkeitsmanagement
Splunk Enterprise/Cloud-Instanz
Entsprechende Splunk-Anmeldeinformationen mit den erforderlichen Berechtigungen
Klonen Sie das Repository:
git clone <repository-url>
cd splunk-mcpInstallieren Sie Abhängigkeiten mit Poetry:
poetry installKopieren Sie die Beispielumgebungsdatei und konfigurieren Sie Ihre Einstellungen:
cp .env.example .envAktualisieren Sie die .env Datei mit Ihren Splunk-Anmeldeinformationen:
SPLUNK_HOST=your_splunk_host
SPLUNK_PORT=8089
SPLUNK_USERNAME=your_username
SPLUNK_PASSWORD=your_password
SPLUNK_SCHEME=https
VERIFY_SSL=true
FASTMCP_LOG_LEVEL=INFORufen Sie das neueste Bild ab:
docker pull livehybrid/splunk-mcp:latestErstellen Sie Ihre .env Datei wie oben beschrieben oder verwenden Sie Umgebungsvariablen direkt.
Mit Docker Compose ausführen:
docker-compose up -dOder direkt mit Docker:
docker run -i \
--env-file .env \
livehybrid/splunk-mcpDas Tool kann in drei Modi ausgeführt werden:
SSE-Modus (Standard für MCP-Clients):
# Start in SSE mode (default)
poetry run python splunk_mcp.py
# or explicitly:
poetry run python splunk_mcp.py sse
# Use uvicorn directly:
SERVER_MODE=api poetry run uvicorn splunk_mcp:app --host 0.0.0.0 --port 8000 --reloadSTDIO-Modus:
poetry run python splunk_mcp.py stdioDas Projekt unterstützt sowohl die neuen Befehle docker compose “ (V2) als auch docker-compose (V1). Die folgenden Beispiele verwenden die Syntax von V2, aber beide werden unterstützt.
SSE-Modus (Standard):
docker compose up -d mcpAPI-Modus:
docker compose run --rm mcp python splunk_mcp.py apiSTDIO-Modus:
docker compose run -i --rm mcp python splunk_mcp.py stdioDas Projekt umfasst eine dedizierte Testumgebung in Docker:
Führen Sie alle Tests aus:
./run_tests.sh --dockerFühren Sie bestimmte Testkomponenten aus:
# Run only the MCP server
docker compose up -d mcp
# Run only the test container
docker compose up test
# Run both with test results
docker compose up --abort-on-container-exitDie Testergebnisse sind im Verzeichnis ./test-results verfügbar.
Gebäudebilder :
# Build both images
docker compose build
# Build specific service
docker compose build mcp
docker compose build testProtokolle anzeigen :
# View all logs
docker compose logs
# Follow specific service logs
docker compose logs -f mcpDebuggen :
# Run with debug mode
DEBUG=true docker compose up mcp
# Access container shell
docker compose exec mcp /bin/bashHinweis: Wenn Sie Docker Compose V1 verwenden, ersetzen Sie in den obigen Befehlen docker compose durch docker-compose .
Umgebungsvariablen :
Übernehmen Sie niemals .env Dateien
Verwenden Sie .env.example als Vorlage
Erwägen Sie die Verwendung von Docker-Geheimnissen für die Produktion
SSL-Verifizierung :
VERIFY_SSL=true empfohlen für die Produktion
Kann für Entwicklung/Tests deaktiviert werden
Konfigurieren über Umgebungsvariablen
Hafenlage :
Nur die erforderlichen Ports offen legen
Verwenden Sie nach Möglichkeit das interne Docker-Netzwerk
Berücksichtigen Sie die Netzwerksicherheit in der Produktion
Konfigurieren Sie die folgenden Umgebungsvariablen:
SPLUNK_HOST : Ihre Splunk-Hostadresse
SPLUNK_PORT : Splunk-Verwaltungsport (Standard: 8089)
SPLUNK_USERNAME : Ihr Splunk-Benutzername
SPLUNK_PASSWORD : Ihr Splunk-Passwort
SPLUNK_SCHEME : Verbindungsschema (Standard: https)
VERIFY_SSL : SSL-Verifizierung aktivieren/deaktivieren (Standard: true)
FASTMCP_LOG_LEVEL : Protokollierungsebene (Standard: INFO)
SERVER_MODE : Servermodus (sse, api, stdio) bei Verwendung von uvicorn
Das Tool bietet flexible SSL-Verifizierungsoptionen:
Standardmodus (Sicherer Modus) :
VERIFY_SSL=trueVollständige SSL-Zertifikatsüberprüfung
Hostnamenüberprüfung aktiviert
Empfohlen für Produktionsumgebungen
Entspannter Modus :
VERIFY_SSL=falseSSL-Zertifikatsüberprüfung deaktiviert
Hostnamenüberprüfung deaktiviert
Nützlich zum Testen oder für selbstsignierte Zertifikate
Das Projekt umfasst eine umfassende Testabdeckung mit pytest und End-to-End-Tests mit einem benutzerdefinierten MCP-Client:
Grundlegende Testausführung:
poetry run pytestMit Berichterstattung über die Berichterstattung:
poetry run pytest --cov=splunk_mcpMit ausführlicher Ausgabe:
poetry run pytest -vDas Projekt umfasst ein benutzerdefiniertes MCP-Client-Testskript, das eine Verbindung zum Live-SSE-Endpunkt herstellt und alle Tools testet:
# Test all tools
python test_endpoints.py
# Test specific tools
python test_endpoints.py health_check list_indexes
# List all available tools
python test_endpoints.py --listDieses Skript fungiert als MCP-Client, indem es:
Herstellen einer Verbindung zum /sse -Endpunkt, um die Nachrichten-URL abzurufen
Senden von Toolaufrufen an den Nachrichtenendpunkt
Verarbeiten der SSE-Ereignisse zum Extrahieren von Toolergebnissen
Validieren der Ergebnisse anhand der erwarteten Formate
Dies ermöglicht praxisnahe Tests der SSE-Schnittstelle, wie sie von einem tatsächlichen MCP-Client verwendet würde.
Das Projekt verwendet drei sich ergänzende Testansätze:
MCP-Integrationstests ( tests/test_api.py ) :
Testet die MCP-Tools-Schnittstelle über mcp.call_tool()
Überprüft die ordnungsgemäße Werkzeugregistrierung mit FastMCP
Stellt das richtige Antwortformat und die richtige Datenstruktur sicher
Validiert die Fehlerbehandlung auf MCP-Schnittstellenebene
Hinweis: Diese Datei sollte idealerweise in test_mcp.py umbenannt werden, um ihren Zweck besser widerzuspiegeln
Direkte Funktionstests ( tests/test_endpoints_pytest.py ) :
Testet Splunk-Funktionen direkt (unter Umgehung der MCP-Schicht)
Bietet eine umfassendere Abdeckung der Details der Funktionsimplementierung
Testet Randfälle, Parametervariationen und Fehlerbehandlung
Beinhaltet Tests für SSL-Konfiguration, Verbindungsparameter und Timeouts
Verwendet parametrisiertes Testen für eine effiziente Testabdeckung
End-to-End-MCP-Clienttests ( test_endpoints.py ) :
Verhält sich wie ein echter MCP-Client, der eine Verbindung zum SSE-Endpunkt herstellt
Testet den gesamten Ablauf von der Verbindung über den Tool-Aufruf bis hin zur Antwortanalyse
Validiert die tatsächliche Implementierung des SSE-Protokolls
Testet Tools mit realen Parametern gegen den Live-Server
Konfigurationstests ( tests/test_config.py ) :
Tests zum Parsen von Umgebungsvariablen
SSL-Verifizierungseinstellungen
Validierung der Verbindungsparameter
Die Tests unterstützen:
Asynchrones Testen mit pytest-asyncio
Abdeckungsberichte mit pytest-cov
Mocking mit pytest-mock
Parametrisiertes Testen
Testen des Verbindungstimeouts
Grundlegende Konnektivität :
Das Tool führt nun einen grundlegenden TCP-Konnektivitätstest durch
Überprüfen Sie, ob Port 8089 erreichbar ist
Überprüfen Sie das Netzwerk-Routing und die Firewalls
SSL-Probleme :
Wenn SSL-Fehler auftreten, versuchen Sie VERIFY_SSL=false festzulegen.
Überprüfen Sie die Gültigkeit des Zertifikats und die Vertrauenskette
Überprüfen, ob der Hostname mit dem Zertifikat übereinstimmt
Authentifizierungsprobleme :
Überprüfen der Splunk-Anmeldeinformationen
Überprüfen Sie die Benutzerberechtigungen
Stellen Sie sicher, dass das Konto nicht gesperrt ist
Debuggen :
Setzen Sie FASTMCP_LOG_LEVEL=DEBUG für detaillierte Protokolle
Überprüfen Sie die Verbindungsprotokolle auf bestimmte Fehlermeldungen
Überprüfen der SSL-Konfigurationsmeldungen
SSE-Verbindungsprobleme :
Überprüfen Sie, ob der SSE-Endpunkt über /sse erreichbar ist.
Überprüfen Sie, ob die Inhaltstyp-Header korrekt sind.
Verwenden Sie Browser-Entwicklertools, um SSE-Verbindungen zu überprüfen
Sie können Splunk MCP in Claude Desktop integrieren, indem Sie es für den SSE- oder STDIO-Modus konfigurieren. Fügen Sie Ihrer claude_desktop_config.json die folgende Konfiguration hinzu:
{
"mcpServers": {
"splunk": {
"command": "poetry",
"env": {
"SPLUNK_HOST": "your_splunk_host",
"SPLUNK_PORT": "8089",
"SPLUNK_USERNAME": "your_username",
"SPLUNK_PASSWORD": "your_password",
"SPLUNK_SCHEME": "https",
"VERIFY_SSL": "false"
},
"args": [
"--directory",
"/path/to/splunk-mcp",
"run",
"python",
"splunk_mcp.py",
"stdio"
]
}
}
}{
"mcpServers": {
"splunk": {
"command": "poetry",
"env": {
"SPLUNK_HOST": "your_splunk_host",
"SPLUNK_PORT": "8089",
"SPLUNK_USERNAME": "your_username",
"SPLUNK_PASSWORD": "your_password",
"SPLUNK_SCHEME": "https",
"VERIFY_SSL": "false",
"FASTMCP_PORT": "8001",
"DEBUG": "true"
},
"args": [
"--directory",
"/path/to/splunk-mcp",
"run",
"python",
"splunk_mcp.py",
"sse"
]
}
}
}Nach der Konfiguration können Sie über Claude in natürlicher Sprache mit Splunk interagieren. Beispiele:
Liste der verfügbaren Indizes:
What Splunk indexes are available?Splunk-Daten durchsuchen:
Search Splunk for failed login attempts in the last 24 hoursSystemzustand abrufen:
Check the health of the Splunk systemKV-Stores verwalten:
List all KV store collectionsDie MCP-Tools stehen Claude automatisch zur Verfügung, sodass er diese Vorgänge über Befehle in natürlicher Sprache ausführen kann.
[Ihre Lizenz hier]
FastMCP-Framework
Splunk SDK für Python
Python-Decouple für das Konfigurationsmanagement
SSE Starlette für die SSE-Implementierung
Get information about the currently authenticated user.
This endpoint retrieves:
- Basic user information (username, real name, email)
- Assigned roles
- Default app settings
- User type
Returns:
Dict[str, Any]: Dictionary containing user information
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes what information is retrieved (e.g., basic info, roles, settings) and the return type, but lacks details on authentication requirements, error handling, or rate limits. This is adequate but has gaps for a tool with no 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 well-structured and front-loaded, starting with the core purpose followed by bullet points for retrieved information and return details. Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is largely complete, covering purpose, retrieved data, and return type. However, it could benefit from more behavioral context, such as authentication notes, to fully compensate for the lack of annotations.
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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's function and output, which aligns with the schema's simplicity.
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 explicitly states the verb 'Get' and resource 'information about the currently authenticated user,' making the purpose specific and clear. It distinguishes this tool from siblings like 'list_users' by focusing on the current authenticated user rather than listing all users.
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 implies usage context by specifying 'currently authenticated user,' suggesting it should be used when user-specific data is needed. However, it does not explicitly state when to use alternatives like 'list_users' or provide exclusions, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all indexes and their sourcetypes.
This endpoint performs a search to gather:
- All available indexes
- All sourcetypes within each index
- Event counts for each sourcetype
- Time range information
Returns:
Dict[str, Any]: Dictionary containing:
- indexes: List of all accessible indexes
- sourcetypes: Dictionary mapping indexes to their sourcetypes
- metadata: Additional information about the search
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (performs a search to gather specific data types) and outlines the return structure in detail. However, it doesn't mention potential limitations like performance impact, authentication requirements, or rate limits, which would be helpful for a search operation.
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 perfectly structured: a clear purpose statement followed by bullet points detailing what the search gathers and a formatted Returns section. Every sentence adds value with zero redundancy, making it easy to scan and understand.
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 zero-parameter tool with no annotations or output schema, the description provides excellent context about the operation and return values. It explains the search behavior and output structure thoroughly. The only minor gap is lack of information about potential side effects or performance characteristics.
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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics instead, detailing what data is returned. This exceeds the baseline expectation for a zero-parameter tool.
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 specific action ('Get a list') and resources ('all indexes and their sourcetypes'), distinguishing it from siblings like list_indexes (which likely just lists indexes) and get_index_info (which likely provides detailed info about a specific index). The verb+resource combination is precise and unambiguous.
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 implies usage for gathering comprehensive index and sourcetype data with event counts and time ranges, but doesn't explicitly state when to use this tool versus alternatives like list_indexes or get_index_info. There's no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get metadata for a specific Splunk index.
Args:
index_name: Name of the index to get metadata for
Returns:
Dictionary containing index metadata
| Name | Required | Description | Default |
|---|---|---|---|
| index_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it 'gets metadata' which implies a read-only operation, but doesn't specify permissions required, rate limits, error conditions, or what specific metadata fields are returned. The return format is vaguely described as a 'dictionary', lacking detail on structure or content.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence serves a purpose: the first states the tool's function, the second explains the parameter, and the third describes the return. No redundant or verbose language is present.
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 read operation with no annotations and no output schema, the description provides basic purpose and parameter explanation but lacks sufficient behavioral context. It doesn't detail what metadata is included, error handling, or authentication requirements, leaving gaps for an agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining the single parameter 'index_name' as 'Name of the index to get metadata for', adding semantic meaning beyond the schema's title 'Index Name'. However, it doesn't provide format examples, constraints, or validation rules for the index name parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'metadata for a specific Splunk index', making the purpose unambiguous. It distinguishes from siblings like 'list_indexes' (which lists multiple indexes) by focusing on metadata retrieval for a single specified index. However, it doesn't explicitly contrast with 'get_indexes_and_sourcetypes' which might overlap in functionality.
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 implies usage when you need metadata for a specific index (vs. listing indexes), but doesn't provide explicit guidance on when to choose this tool over alternatives like 'get_indexes_and_sourcetypes' or 'list_indexes'. No prerequisites, exclusions, or comparative context are mentioned, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get basic Splunk connection information and list available apps (same as health_check but for endpoint consistency)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does (get connection info and list apps) but lacks details on permissions required, rate limits, error conditions, or response format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 perfectly concise and front-loaded: the first clause states the core purpose, and the second provides important sibling context. Every sentence earns its place with zero wasted words 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?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete for a basic health/status tool. However, without annotations or output schema, it should ideally mention what specific information is returned or any authentication requirements. The sibling comparison adds useful context.
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 has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct. Baseline for 0 parameters 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 states the tool's purpose: 'Get basic Splunk connection information and list available apps'. It specifies both the verb ('Get') and the resources ('connection information', 'available apps'), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'health_check' beyond noting they're functionally equivalent for endpoint consistency.
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 clear context by stating this tool is 'same as health_check but for endpoint consistency', which implicitly guides usage toward this tool when endpoint consistency is needed. It doesn't explicitly state when NOT to use it or name alternatives beyond the mentioned sibling, but the comparison offers practical guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get basic Splunk connection information and list available apps
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it states what the tool does, it doesn't describe important behavioral aspects like authentication requirements, rate limits, error conditions, or what specific information constitutes 'basic Splunk connection information.' The description is functional but lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the complete purpose without any wasted words. It's appropriately sized for a zero-parameter diagnostic tool and front-loads the essential information. Every word earns its place in the description.
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 zero parameters and no output schema, the description adequately covers what the tool does but leaves important questions unanswered about return format, error handling, and authentication requirements. For a diagnostic tool that likely returns structured data about system health, more detail about what constitutes 'basic connection information' would be helpful.
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 has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct for this case. A baseline of 4 is appropriate when no parameters need documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get', 'list') and resources ('basic Splunk connection information', 'available apps'), making it immediately understandable. It distinguishes this tool from siblings like 'ping' (simple connectivity) or 'list_users' (specific resource listing) by combining connection verification with app inventory.
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 implies usage for checking Splunk connectivity and discovering apps, but doesn't explicitly state when to use this versus alternatives like 'ping' (simpler connectivity check) or 'list_saved_searches' (specific resource listing). No guidance is provided about when NOT to use this tool or what prerequisites might be needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all available Splunk indexes.
Returns:
Dictionary containing list of indexes
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool returns a dictionary with a list of indexes, which is basic behavioral information. However, it lacks details on permissions, rate limits, or whether the list is paginated or filtered, which are important for a read operation in a system like Splunk.
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 concise with two sentences that directly state the purpose and return value. It's front-loaded with the main action, though it could be slightly more structured by explicitly separating usage notes from return 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?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and return format, but for a tool in a complex system like Splunk, it could benefit from more context on limitations or typical use cases.
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 has 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description doesn't add parameter details, but this is acceptable given the lack of parameters, aligning with the baseline for 0 parameters.
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's purpose with 'Get a list of all available Splunk indexes,' which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_indexes_and_sourcetypes' or 'get_index_info,' which might offer overlapping or related functionality.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for a simple list versus more detailed index information available in other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all KV store collections across apps.
Returns:
List of KV store collections with metadata including app, fields, and accelerated fields
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists collections and describes the return format, which is helpful. However, it lacks details on potential side effects, authentication needs, rate limits, or error handling, leaving gaps in behavioral context for a tool that might involve data access across apps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with only two sentences, front-loading the purpose and then detailing the return format. Every sentence earns its place by providing essential information without any wasted words 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?
Given the tool's complexity (listing collections across apps) and the absence of annotations and output schema, the description is minimally adequate. It covers the purpose and return format, but for a tool that might involve cross-app data access, more context on permissions, scope, or limitations would enhance completeness.
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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on the return values instead, adding value by specifying what metadata is included in the output, which compensates for the lack of an output schema.
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 action ('List all KV store collections') and the resource ('across apps'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'list_indexes' or 'list_saved_searches', which would require mentioning what makes KV store collections distinct from those other resources.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or compare it to sibling tools like 'get_indexes_and_sourcetypes' or 'list_indexes', leaving the agent without explicit direction on selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all saved searches in Splunk
Returns:
List of saved searches with their names, descriptions, and search queries
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the return format ('List of saved searches with their names, descriptions, and search queries'), which is helpful, but lacks details on permissions, rate limits, pagination, or error handling for a read operation in Splunk.
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 front-loaded with the core purpose in the first sentence and adds return details in a second sentence. It's efficient with zero waste, though slightly brief for a tool with no annotations, which keeps it from a perfect score.
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 0 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It explains what the tool does and the return format, but for a read operation in Splunk with no annotations, it could benefit from more behavioral context like access requirements or data scope.
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 has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately omits parameter details, earning a high baseline score for not adding unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all saved searches in Splunk'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'list_indexes' or 'list_users' beyond specifying the resource type, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, limitations, or comparisons to sibling tools like 'search_splunk' or 'get_indexes_and_sourcetypes', leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available MCP tools.
Returns:
List of all available tools with their name, description, and parameters.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the return format ('List of all available tools with their name, description, and parameters'), which is helpful. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or pagination behavior. The description provides some output information but misses key operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured. Two sentences efficiently convey the tool's purpose and return format with zero wasted words. It's front-loaded with the core functionality and follows with return 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?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does and what it returns, but doesn't provide context about when to use it or behavioral constraints. For a tool with no structured metadata, the description should do more to compensate, particularly around usage guidelines and operational behavior.
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 has 0 parameters with 100% schema description coverage. The description doesn't need to explain parameters since none exist. It appropriately focuses on the tool's function and return value rather than parameter details. The baseline for 0 parameters 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 states the tool's purpose: 'List all available MCP tools' with a specific verb ('List') and resource ('MCP tools'). It distinguishes from siblings by focusing on tools rather than users, indexes, searches, etc. However, it doesn't explicitly differentiate from similar listing tools like list_indexes or list_users beyond the resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, timing considerations, or compare it to other listing tools. It simply states what it does without context about when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all Splunk users (requires admin privileges)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the admin privilege requirement, which is useful behavioral context. However, it doesn't mention other traits like pagination, rate limits, or return format, leaving gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes essential context (admin privileges) without unnecessary elaboration.
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 0 parameters and no output schema, the description covers the basic purpose and permission requirement adequately. However, without annotations or output schema, it lacks details on return format (e.g., list structure, fields) and other behavioral aspects, making it minimally complete.
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 has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline 4 for this context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all Splunk users'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'current_user' or 'list_indexes', which would require a 5.
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 clear context with 'requires admin privileges', indicating when this tool is appropriate. It doesn't explicitly state when not to use it or name alternatives like 'current_user' for non-admin scenarios, preventing a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Simple ping endpoint to check server availability and get basic server information.
This endpoint provides a lightweight way to:
- Verify the server is running and responsive
- Get basic server information including version and server time
- Check connectivity without making complex API calls
Returns:
Dict[str, Any]: Dictionary containing status and basic server information
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing behavioral traits: it's a read-only operation that returns server information, doesn't modify anything, and provides version and server time. It could mention response format details or error behavior, but covers the essential safety profile adequately.
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 perfectly structured: opening statement of purpose, bulleted list of specific capabilities, and clear return value description. Every sentence earns its place with zero wasted words, and information is front-loaded effectively.
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 zero-parameter diagnostic tool with no annotations and no output schema, the description provides complete context: purpose, when to use, what it returns, and how it differs from other tools. The return format description ('Dict[str, Any]') compensates for the missing output schema.
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 has 0 parameters with 100% schema coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and behavior.
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's purpose with specific verbs ('check server availability', 'get basic server information') and distinguishes it from siblings by emphasizing it's a 'lightweight way' for connectivity verification rather than complex API operations like search_splunk or list_users.
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 states when to use this tool: 'to check server availability', 'verify the server is running and responsive', and 'check connectivity without making complex API calls'. It clearly distinguishes this from more complex operations available in sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a Splunk search query and return the results.
Args:
search_query: The search query to execute
earliest_time: Start time for the search (default: 24 hours ago)
latest_time: End time for the search (default: now)
max_results: Maximum number of results to return (default: 100)
Returns:
List of search results
| Name | Required | Description | Default |
|---|---|---|---|
| search_query | Yes | ||
| earliest_time | No | -24h | |
| latest_time | No | now | |
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool executes a search and returns results, but lacks details on permissions required, rate limits, whether it's read-only or has side effects, response format, or error handling. For a tool with 4 parameters and no annotation coverage, this is a significant gap in transparency.
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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by a bulleted list of args with explanations and defaults, and ends with return information. Every sentence earns its place by adding value, with no redundant or verbose content, making it easy to parse and understand.
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 tool's complexity (4 parameters, no annotations, no output schema), the description is partially complete. It covers parameter semantics well but lacks behavioral context like permissions or rate limits, and without an output schema, it doesn't detail the structure of the 'List of search results'. This leaves gaps for an AI agent to fully understand tool behavior and outputs.
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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'search_query' as the query to execute, 'earliest_time' and 'latest_time' as time bounds with defaults, and 'max_results' as a limit. This compensates fully for the schema's lack of descriptions, providing clear semantics for all parameters.
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's purpose: 'Execute a Splunk search query and return the results.' This specifies the verb ('execute') and resource ('Splunk search query'), making it understandable. However, it doesn't explicitly differentiate this tool from potential siblings like 'list_saved_searches' or 'get_indexes_and_sourcetypes', which might also involve search-related operations, so it falls short of a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_saved_searches' for retrieving pre-defined searches or 'get_indexes_and_sourcetypes' for exploring data sources, nor does it specify prerequisites or contexts for executing searches. This lack of comparative information limits its utility for an AI agent in selecting the right tool.
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.
current_userget_index_infoget_indexes_and_sourcetypeshealthhealth_checklist_indexeslist_kvstore_collectionslist_saved_searcheslist_toolslist_userspingsearch_splunkScored across 12 tools
There is significant overlap between several tools, which could cause confusion. For example, 'health' and 'health_check' appear to be duplicates, and 'list_indexes', 'get_indexes_and_sourcetypes', and 'get_index_info' all relate to indexes with unclear boundaries. However, descriptions help differentiate some tools, such as 'search_splunk' being distinct for query execution.
Naming is inconsistent with mixed conventions. Some tools use verb_noun patterns like 'list_indexes' and 'search_splunk', while others use noun-only forms like 'health' and 'ping'. There are also deviations like 'get_indexes_and_sourcetypes' using 'and' in the name, and 'list_tools' is an outlier as it's meta to the server itself.
With 12 tools, the count is reasonable for a Splunk MCP server, covering user management, indexing, searches, and health checks. It's slightly heavy due to redundant tools like 'health' and 'health_check', but overall well-scoped for the domain without being overwhelming.
The toolset covers core Splunk operations like searching, indexing, and user management, but has notable gaps. For example, there are tools to list saved searches but no way to create, update, or delete them, and similar gaps exist for KV store collections and indexes. This could lead to dead ends for agents trying to perform full lifecycle management.
Official Microsoft MCP Server to query Microsoft Entra data using natural language
Query your org's data in natural language — read-only MCP access to SQL, NoSQL, files & warehouses.
Your org's AI agents, tasks, runs, search, and brain files as MCP tools and resources.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
