OTRS MCP Server
Servidor MCP de OTRS
Un servidor Model Context Protocol (MCP) para la integración con la API de OTRS (Open Ticket Request System).
Esto proporciona acceso a la gestión de tickets de OTRS a través de interfaces MCP estandarizadas, lo que permite a los asistentes de IA crear, buscar y gestionar tickets.
Características
Crear, leer, actualizar y buscar tickets
Acceder al historial y a la información detallada de los tickets
Valores predeterminados configurables para los tickets
Soporte para contenedores Docker
Soporte SSL/TLS con opciones de verificación de certificados
Proporcionar herramientas interactivas para asistentes de IA
La lista de herramientas es configurable, por lo que puedes elegir qué herramientas quieres poner a disposición del cliente MCP.
Related MCP server: tickiti-mcp
Requisitos previos
Configuración del servidor OTRS
Antes de usar este servidor MCP, necesitas configurar tu instancia de OTRS.
Paso 1: Accede al panel de administración de OTRS
URL:
https://your-otrs-server/otrs/index.pl?Action=AdminInicia sesión con tus credenciales de administrador
Paso 2: Configura los Web Services
Ve a: Administración del sistema → Web Services
Crea o verifica que tienes un servicio web (por ejemplo,
TestInterface) con estas operaciones:✅ SessionCreate
✅ TicketCreate
✅ TicketGet
✅ TicketSearch
✅ TicketUpdate
✅ TicketHistoryGet
Paso 3: Anota la URL de tu servicio web
La URL de tu servicio web debería tener un formato similar a:
https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/YourWebserviceName
Paso 4: Asegura los permisos del usuario
Asegúrate de que tu usuario de OTRS tiene los permisos adecuados para:
Crear y actualizar tickets
Acceder a los elementos de configuración
Usar la interfaz genérica
Uso
Docker (recomendado)
La forma más fácil de ejecutar otrs-mcp con Claude Desktop es usando Docker. Si no tienes Docker instalado, puedes obtenerlo en el sitio web oficial de Docker.
Usar la imagen precompilada
Puedes usar la imagen Docker precompilada de GitHub Container Registry:
{
"mcpServers": {
"otrs": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e",
"OTRS_BASE_URL=https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface",
"-e",
"OTRS_USERNAME=your-username",
"-e",
"OTRS_PASSWORD=your-password",
"-e",
"OTRS_VERIFY_SSL=false",
"-e",
"OTRS_DEFAULT_QUEUE=Raw",
"-e",
"OTRS_DEFAULT_STATE=new",
"-e",
"OTRS_DEFAULT_PRIORITY=3 normal",
"ghcr.io/eduardoantoniojunior/otrs-mcp-server:latest"
]
}
}
}Compilar localmente
Si prefieres compilar la imagen localmente:
# Clone the repository
git clone https://github.com/eduardoantoniojunior/otrs-mcp-server.git
cd otrs-mcp-server
# Build the Docker image
docker build -t otrs-mcp-server .
# Run the container
docker run --rm -i \
-e OTRS_BASE_URL="https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface" \
-e OTRS_USERNAME="your-username" \
-e OTRS_PASSWORD="your-password" \
-e OTRS_VERIFY_SSL="false" \
otrs-mcp-serverEjecutar con UV
Alternativamente, puedes ejecutar el servidor directamente con UV. Primero, configura tus variables de entorno:
export OTRS_BASE_URL="https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface"
export OTRS_USERNAME="your-username"
export OTRS_PASSWORD="your-password"
export OTRS_VERIFY_SSL="false"
export OTRS_DEFAULT_QUEUE="Raw"
export OTRS_DEFAULT_STATE="new"
export OTRS_DEFAULT_PRIORITY="3 normal"
export OTRS_DEFAULT_TYPE="Unclassified"Después, edita el archivo de configuración de Claude Desktop y añade la configuración del servidor:
{
"mcpServers": {
"otrs": {
"command": "uv",
"args": [
"--directory",
"<full path to otrs-mcp-server directory>",
"run",
"src/otrs_mcp/main.py"
],
"env": {
"OTRS_BASE_URL": "https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface",
"OTRS_USERNAME": "your-username",
"OTRS_PASSWORD": "your-password",
"OTRS_VERIFY_SSL": "false"
}
}
}
}Nota: si ves
Error: spawn uv ENOENTen Claude Desktop, puede que necesites especificar la ruta completa deuvo establecer la variable de entornoNO_UV=1en la configuración.
Variables de entorno
Variable | Obligatorio | Por defecto | Descripción |
| ✅ | - | URL base del servicio web de OTRS |
| ✅ | - | Nombre de usuario de OTRS |
| ✅ | - | Contraseña de OTRS |
| ❌ |
| Verificación del certificado SSL |
| ❌ |
| Cola por defecto para los tickets nuevos |
| ❌ |
| Estado por defecto para los tickets nuevos |
| ❌ |
| Prioridad por defecto para los tickets nuevos |
| ❌ |
| Tipo por defecto para los tickets nuevos |
Desarrollo
Las contribuciones son bienvenidas. Si tienes sugerencias o mejoras, abre un issue o crea una pull request.
Este proyecto tiene como objetivo Python 3.12 (ver requires-python en pyproject.toml) y está validado para su uso en producción en esa versión.
Este proyecto usa uv para gestionar las dependencias. Instala uv siguiendo las instrucciones de tu plataforma:
curl -LsSf https://astral.sh/uv/install.sh | shInstala Python 3.12 (si no lo tienes) y crea el entorno virtual con las dependencias fijadas:
# Install the interpreter (managed by uv)
uv python install 3.12
# Create the environment and install dependencies from uv.lock
uv sync --python 3.12 --extra devTambién puedes usar el flujo de trabajo clásico:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv pip install -e .Pruebas
Prueba tu conexión con OTRS y la funcionalidad de la API:
# Set environment variables
export OTRS_BASE_URL="https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface"
export OTRS_USERNAME="your-username"
export OTRS_PASSWORD="your-password"
export OTRS_VERIFY_SSL="false"
# Run connectivity test
uv run python tests/connectivity_test.py
# Run API functionality test
uv run python tests/test_working_api.py
# Run debug diagnostics
uv run python tests/debug_test.pyEl proyecto incluye scripts de prueba que ayudan a verificar tu configuración y la conectividad con la API de OTRS.
Ejecuta las pruebas con pytest:
# Install development dependencies
uv pip install -e ".[dev]"
# Run the tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=term-missingPublicar la imagen Docker
Para publicar la imagen Docker en GitHub Container Registry para de uso público:
Requisitos previos
Cuenta de GitHub con un repositorio para este proyecto
GitHub Personal Access Token con permiso
write:packagesDocker instalado localmente
Publicación paso a paso
Genera un GitHub Personal Access Token:
Ve a GitHub Settings → Developer settings → Personal access tokens → Tokens (classic)
Genera un nuevo token con permisos
write:packagesyread:packagesGuarda el token de forma segura
Inicia sesión en GitHub Container Registry:
echo $GITHUB_TOKEN | docker login ghcr.io -u yourusername --password-stdinCompila y etiqueta la imagen:
# Build the image docker build -t otrs-mcp-server . # Tag for GitHub Container Registry docker tag otrs-mcp-server ghcr.io/yourusername/otrs-mcp-server:latest docker tag otrs-mcp-server ghcr.io/yourusername/otrs-mcp-server:v0.1.0Sube la imagen al registro:
# Push latest tag docker push ghcr.io/yourusername/otrs-mcp-server:latest # Push version tag docker push ghcr.io/yourusername/otrs-mcp-server:v0.1.0Haz público el paquete (opcional):
Ve a tu repositorio de GitHub
Navega a la sección Packages
Haz clic en tu paquete
Ve a Package settings
Cambia la visibilidad a Public
Publicación automatizada con GitHub Actions
Crea .github/workflows/docker-publish.yml:
name: Build and Push Docker Image
on:
push:
branches: [main]
tags: ["v*"]
pull_request:
branches: [main]
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
build-and-push:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Log in to Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}Alternativa: Docker Hub
Para publicar en Docker Hub en su lugar:
# Login to Docker Hub
docker login
# Tag for Docker Hub
docker tag otrs-mcp-server yourusername/otrs-mcp-server:latest
docker tag otrs-mcp-server yourusername/otrs-mcp-server:v0.1.0
# Push to Docker Hub
docker push yourusername/otrs-mcp-server:latest
docker push yourusername/otrs-mcp-server:v0.1.0Luego actualiza la configuración de Claude Desktop para usar:
"ghcr.io/yourusername/otrs-mcp-server:latest"o
"yourusername/otrs-mcp-server:latest"Herramientas disponibles
🎫 Gestión de tickets
create_ticket- Crea un nuevo ticket en OTRSget_ticket- Obtén información detallada de un ticket específicosearch_tickets- Busca tickets según diferentes criteriosupdate_ticket- Actualiza las propiedades de un ticket existenteget_ticket_history- Obtén el historial completo de un ticket
📊 Recursos
otrs://ticket/{ticket_id}- Acceso directo a los datos del ticketotrs://ticket/{ticket_id}/history- Acceso al historial del ticketotrs://search/tickets- Visión general de los tickets recientes
Solución de problemas
Problemas comunes
Errores de certificado SSL: Configura
OTRS_VERIFY_SSL=falsepara certificados autofirmados.Redirecciones HTTP 301: Asegúrate de usar URLs HTTPS si tu servidor OTRS redirige HTTP a HTTPS.
Fallos de autenticación: Verifica tu usuario, tu contraseña y la configuración del servicio web.
Operaciones que faltan: Comprueba que tu servicio web de OTRS incluye todas las operaciones requeridas.
Modo de depuración
Ejecuta el script de depuración para diagnosticar problemas de conexión:
uv run python tests/debug_test.pyEsto probará las conexiones HTTP y HTTPS y proporcionará información detallada sobre los errores.
Ejemplo de configuración que funciona
Para tu referencia, aquí tienes una configuración válida de ejemplo:
# Environment variables
export OTRS_BASE_URL="https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/TestInterface"
export OTRS_USERNAME="your-username"
export OTRS_PASSWORD="your-password"
export OTRS_VERIFY_SSL="false"
export OTRS_DEFAULT_QUEUE="Raw"
export OTRS_DEFAULT_STATE="new"
export OTRS_DEFAULT_PRIORITY="3 normal"
export OTRS_DEFAULT_TYPE="Unclassified"Operaciones del servicio web de OTRS
Tu servicio web de OTRS debe incluir estas operaciones:
Nombre de la operación | Controller | Descripción |
TicketCreate | Ticket::TicketCreate | Crear tickets nuevos |
TicketGet | Ticket::TicketGet | Obtener detalles del ticket |
TicketSearch | Ticket::TicketSearch | Buscar tickets |
TicketUpdate | Ticket::TicketUpdate | Actualizar tickets existentes |
TicketHistoryGet | Ticket::TicketHistoryGet | Obtener el historial del ticket |
Licencia
Apache-2.0
Available Tools
5 toolscreate_ticketC
Create a new ticket in OTRS
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | ||
| queue | No | ||
| state | No | ||
| title | Yes | ||
| priority | No | ||
| ticket_type | No | ||
| customer_user | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing side effects. It only names the action without noting that a persistent OTRS record is created, whether calls are idempotent, whether authentication is required, or what errors may occur.
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 7-word sentence with zero filler and the verb front-loaded. It is appropriately brief, though it borders on under-specification rather than genuine explanatory economy.
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?
A 7-parameter tool with no annotations and no parameter descriptions needs far more than a purpose statement; the description omits the required title/body contract and how optional fields are validated. The presence of an output schema covers return values, which keeps this from a 1, but an agent still lacks essential calling 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% across 7 parameters, so the description must compensate, but it names no parameters at all. An agent is left with bare parameter names (body, queue, state, priority, ticket_type, customer_user) plus title, with no format, allowable-value, or domain guidance.
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?
States a specific verb ('Create') and resource ('a new ticket in OTRS'), and the word 'new' cleanly separates it from the update_ticket sibling. It is accurate and unambiguous, though it adds only the 'in OTRS' context beyond what the tool name already communicates.
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 on when to use this tool versus get_ticket, search_tickets, or update_ticket. No prerequisites, ordering constraints, or when-not-to-use conditions are mentioned; usage context must be entirely inferred from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ticketB
Get ticket details from OTRS
| Name | Required | Description | Default |
|---|---|---|---|
| ticket_id | Yes | ||
| include_extended_data | No | ||
| include_dynamic_fields | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of indicating safety; the word 'Get' clearly conveys a read-only retrieval with no mutation, which is useful. However, it adds no detail about access requirements, behavior of the include flags, or potential response size, though some of this is covered by the output schema.
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, front-loaded with the verb and object, with no filler. It is appropriately sized for a simple retrieval tool.
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 output schema covers return shape, so that burden is lifted. Still, with no annotations and sibling tools present, the description doesn't clarify when to choose this tool or how extended data and dynamic fields affect the result, leaving clear gaps in 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% and the description adds no parameter-specific meaning beyond the raw schema titles. The required ticket_id is inferable from tool name, but include_extended_data and include_dynamic_fields are not explained, making this less than adequate.
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 a get operation on ticket details from OTRS, which is the correct resource and verb. It does not explicitly contrast with siblings such as get_ticket_history or search_tickets, so it stops short of the top 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 given about when to use this tool over search_tickets, get_ticket_history, or other siblings. The meaning is inferable but not stated; an agent is not told that this is for fetching a single known ticket by ID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ticket_historyC
Get ticket history from OTRS
| Name | Required | Description | Default |
|---|---|---|---|
| ticket_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It indicates a read operation but does not disclose what 'history' includes, whether it returns change logs versus messages, what auth or prerequisites are needed, or how results are ordered/limited.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no filler. It could carry more useful detail, but it is well-structured and front-loaded for a simple tool.
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 one parameter and an output schema available, the basic invocation path is reasonably clear. However, the description lacks enough detail about what constitutes ticket history and under what circumstances to choose this over get_ticket, leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the ticket_id parameter beyond its name and string type. The parameter name is fairly self-explanatory, but the description adds no semantic value to compensate for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Get'), a specific resource ('ticket history'), and the system ('OTRS'). It is distinguishable from siblings like get_ticket by name and resource, though it does not explicitly contrast itself with them.
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?
There is no guidance on when to use this tool versus get_ticket or search_tickets. The intended context is implied by the name and description, but no exclusions, alternatives, or conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_ticketsC
Search for tickets in OTRS
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| queue | No | ||
| state | No | ||
| title | No | ||
| sort_by | No | Age | |
| order_by | No | Down | |
| priority | No | ||
| customer_id | No | ||
| customer_user | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full disclosure burden, yet it reveals nothing about match semantics, how filters combine, the default sort (Age/Down), or limit/pagination behavior. The output schema helps with return shape but not with behavioral traits an agent needs to predict the tool's effect.
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 single sentence is lean and front-loaded with the core action, but for a tool with 9 optional parameters, this brevity is under-specification rather than efficient structure. There are no wasted words, yet no additional context is packed into the available space.
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 moderate complexity (9 optional filters, no annotations, 0% schema coverage), the description is far too thin to support correct invocation. An agent would not know how filters behave, what values queue/state accept, or how sorting works; the output schema covers only what is returned, not how to call the tool properly.
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% across 9 parameters, and the description does nothing to compensate. It never mentions the filter parameters, does not explain valid values for sort_by/order_by, and does not clarify the distinction between customer_id and customer_user, leaving the agent to guess parameter meaning.
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 ('Search') and resource ('tickets in OTRS'), clearly identifying the tool's core action. The sibling tools use distinct verbs (create, get, update, get_history), so search is implicitly differentiated as the query-multiple-tickets operation, though no explicit contrast is given.
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. An agent must infer that get_ticket retrieves a single ticket while search_tickets finds tickets by criteria, but nothing states this, and no exclusions or alternative recommendations are offered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_ticketC
Update an existing ticket in OTRS
| Name | Required | Description | Default |
|---|---|---|---|
| owner | No | ||
| queue | No | ||
| state | No | ||
| title | No | ||
| priority | No | ||
| ticket_id | Yes | ||
| customer_user | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral details on its own. It communicates that the tool mutates an existing ticket, but it does not explain whether updates are partial, what fields are affected, what authorization is needed, or how invalid ticket IDs are handled.
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 with no filler or redundancy. It is concise, though its brevity contributes to the under-specification penalized in other dimensions.
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 7 parameters and no annotation or schema descriptions, one short sentence is insufficient. The description covers the core operation but omits field-level update semantics, optionality behavior, and error conditions; the presence of an output schema only accounts for return values, not input 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?
Schema description coverage is 0% and the description adds no parameter information. The 7 parameters, including optional nullable fields like state, priority, and owner, are left entirely to the agent to infer from their titles, so the description fails to compensate for the schema's lack of explanations.
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: 'Update an existing ticket in OTRS'. It clearly indicates this tool modifies an already-created ticket, which distinguishes it from create_ticket and the read-only siblings, but it does not enumerate the updatable fields or explicitly name alternatives.
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?
Usage is only implied: use this tool when an existing OTRS ticket needs to be modified. There is no explicit guidance on when to prefer it over create_ticket or get_ticket, and no conditions, prerequisites, or exclusions are provided.
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.
5 tool updates
v0.2.0- First observed
create_ticket - First observed
get_ticket - First observed
get_ticket_history - First observed
search_tickets - First observed
update_ticket
TDQS
Scored across 5 tools
Each tool targets a distinct operation on tickets: create, retrieve individual details, search, update, and retrieve history. There is no meaningful overlap between get_ticket and get_ticket_history because one returns current state and the other returns the audit trail.
All tools follow a consistent verb_noun pattern with snake_case: create_ticket, get_ticket, search_tickets, update_ticket, get_ticket_history. The only minor variation is the plural 'tickets' in search_tickets, which is natural and does not break the pattern.
Five tools is well-scoped for a ticket management server. Each tool represents a core operation without unnecessary redundancy, making the set easy to navigate.
The tool surface covers the essential ticket lifecycle: creation, retrieval, search, update, and history lookup. For OTRS, this is a complete and practical set with no obvious dead ends.
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