OTRS MCP Server
OTRS MCP Server
Ein Model Context Protocol-Server (MCP) für die OTRS-API-Integration (Open Ticket Request System).
Dies bietet Zugriff auf die OTRS-Ticketverwaltung über standardisierte MCP-Schnittstellen und ermöglicht es KI-Assistenten, Tickets zu erstellen, zu suchen und zu verwalten.
Funktionen
Tickets erstellen, lesen, aktualisieren und suchen
Zugriff auf Ticketverlauf und detaillierte Informationen
Konfigurierbare Standardwerte für Tickets
Unterstützung für Docker-Containerisierung
SSL/TLS-Unterstützung mit Optionen zur Zertifikatsprüfung
Bereitstellung interaktiver Tools für KI-Assistenten
Die Liste der Tools ist konfigurierbar, sodass Sie auswählen können, welche Tools Sie dem MCP-Client zur Verfügung stellen möchten.
Related MCP server: tickiti-mcp
Voraussetzungen
OTRS-Serverkonfiguration
Bevor Sie diesen MCP-Server verwenden, müssen Sie Ihre OTRS-Instanz konfigurieren:
Schritt 1: OTRS-Admin-Panel aufrufen
URL:
https://your-otrs-server/otrs/index.pl?Action=AdminMelden Sie sich mit Ihren Admin-Zugangsdaten an
Schritt 2: Web Services konfigurieren
Navigieren Sie zu: Systemadministration → Web Services
Erstellen Sie einen Webservice (z. B. „TestInterface") oder verifizieren Sie, dass Sie einen mit den folgenden Operationen haben:
✅ SessionCreate
✅ TicketCreate
✅ TicketGet
✅ TicketSearch
✅ TicketUpdate
✅ TicketHistoryGet
Schritt 3: Notieren Sie Ihre Webservice-URL
Ihre Webservice-URL sollte wie folgt aussehen:
https://your-otrs-server/otrs/nph-genericinterface.pl/Webservice/YourWebserviceName
Schritt 4: Benutzerberechtigungen sicherstellen
Stellen Sie sicher, dass Ihr OTRS-Benutzer über entsprechende Berechtigungen verfügt für:
Erstellen und Aktualisieren von Tickets
Zugriff auf Konfigurationselemente
Verwendung der Generic Interface
Verwendung
Docker (empfohlen)
Der einfachste Weg, otrs-mcp mit Claude Desktop auszuführen, ist Docker. Wenn Sie Docker nicht installiert haben, können Sie es von der offiziellen Docker-Website herunterladen.
Verwendung des vorgefertigten Images
Sie können das vorgefertigte Docker-Image aus dem GitHub Container Registry verwenden:
{
"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"
]
}
}
}Lokales Erstellen
Wenn Sie das Image lieber lokal erstellen möchten:
# 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-serverAusführung mit UV
Alternativ können Sie den Server direkt mit UV ausführen. Setzen Sie zunächst Ihre Umgebungsvariablen:
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"Bearbeiten Sie dann Ihre Claude-Desktop-Konfigurationsdatei und fügen Sie die Serverkonfiguration hinzu:
{
"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"
}
}
}
}Hinweis: Wenn Sie
Error: spawn uv ENOENTin Claude Desktop sehen, müssen Sie möglicherweise den vollständigen Pfad zuuvangeben oder die UmgebungsvariableNO_UV=1in der Konfiguration setzen.
Umgebungsvariablen
Variable | Erforderlich | Standard | Beschreibung |
| ✅ | - | Basis-URL für den OTRS-Webservice |
| ✅ | - | OTRS-Benutzername |
| ✅ | - | OTRS-Passwort |
| ❌ |
| SSL-Zertifikatsprüfung aktivieren |
| ❌ |
| Standard-Warteschlange für neue Tickets |
| ❌ |
| Standardstatus für neue Tickets |
| ❌ |
| Standardpriorität für neue Tickets |
| ❌ |
| Standardtyp für neue Tickets |
Entwicklung
Beiträge sind willkommen! Bitte öffnen Sie ein Issue oder reichen Sie einen Pull Request ein, wenn Sie Vorschläge oder Verbesserungen haben.
Dieses Projekt zielt auf Python 3.12 ab (siehe requires-python in pyproject.toml) und ist für den Produktionseinsatz auf dieser Version validiert.
Dieses Projekt verwendet uv zur Verwaltung von Abhängigkeiten. Installieren Sie uv gemäß den Anweisungen für Ihre Plattform:
curl -LsSf https://astral.sh/uv/install.sh | shInstallieren Sie Python 3.12 (falls nicht vorhanden) und erstellen Sie die virtuelle Umgebung mit den festgelegten Abhängigkeiten:
# 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 devAlternativ mit dem klassischen Workflow:
uv venv --python 3.12
source .venv/bin/activate # On Unix/macOS
.venv\Scripts\activate # On Windows
uv pip install -e .Testen
Testen Sie Ihre OTRS-Verbindung und API-Funktionalität:
# 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.pyDas Projekt enthält Testskripte, die helfen, Ihre OTRS-Konfiguration und API-Konnektivität zu überprüfen.
Führen Sie die Tests mit pytest aus:
# Install development dependencies
uv pip install -e ".[dev]"
# Run the tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=term-missingVeröffentlichen des Docker-Images
So veröffentlichen Sie das Docker-Image im GitHub Container Registry zur öffentlichen Nutzung:
Voraussetzungen
GitHub-Konto mit einem Repository für dieses Projekt
GitHub Personal Access Token mit
write:packages-BerechtigungDocker lokal installiert
Schritt-für-Schritt-Veröffentlichung
GitHub Personal Access Token erstellen:
Gehen Sie zu GitHub-Einstellungen → Entwicklereinstellungen → Personal Access Tokens → Tokens (klassisch)
Generieren Sie ein neues Token mit
write:packages- undread:packages-BerechtigungenSpeichern Sie das Token sicher
Beim GitHub Container Registry anmelden:
echo $GITHUB_TOKEN | docker login ghcr.io -u yourusername --password-stdinImage erstellen und taggen:
# 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:v0.1.0In das Registry pushen:
# Push latest tag docker push ghcr.io/yourusername/otrs-mcp-server:latest # Push version tag docker push ghcr.io/yourusername/otrs-mcp-server:v0.1.0Paket öffentlich machen (optional):
Gehen Sie zu Ihrem GitHub-Repository
Navigieren Sie zum Abschnitt „Packages"
Klicken Sie auf Ihr Paket
Gehen Sie zu „Package settings"
Ändern Sie die Sichtbarkeit auf „Public"
Automatisierte Veröffentlichung mit GitHub Actions
Erstellen Sie .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 }}Alternative: Docker Hub
Um stattdessen auf Docker Hub zu veröffentlichen:
# 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:v0.1.0Aktualisieren Sie dann die Claude-Desktop-Konfiguration, um Folgendes zu verwenden:
"ghcr.io/yourusername/otrs-mcp-server:latest"oder
"yourusername/otrs-mcp-server:latest"Verfügbare Tools
🎫 Ticketverwaltung
create_ticket– Ein neues Ticket in OTRS erstellenget_ticket– Detaillierte Informationen zu einem bestimmten Ticket abrufensearch_tickets– Tickets anhand verschiedener Kriterien suchenupdate_ticket– Eigenschaften eines vorhandenen Tickets aktualisierenget_ticket_history– Den vollständigen Verlauf eines Tickets abrufen
📊 Ressourcen
otrs://ticket/{ticket_id}– Direkter Zugriff auf Ticketdatenotrs://ticket/{ticket_id}/history– Zugriff auf den Ticketverlaufotrs://search/tickets– Übersicht über aktuelle Tickets
Fehlerbehebung
Häufige Probleme
SSL-Zertifikatsfehler: Setzen Sie
OTRS_VERIFY_SSL=falsefür selbstsignierte ZertifikateHTTP-301-Weiterleitungen: Stellen Sie sicher, dass Sie HTTPS-URLs verwenden, wenn Ihr OTRS-Server HTTP auf HTTPS umleitet
Authentifizierungsfehler: Überprüfen Sie Benutzername, Passwort und Webservice-Konfiguration
Fehlende Operationen: Stellen Sie sicher, dass Ihr OTRS-Webservice alle erforderlichen Operationen enthält
Debug-Modus
Führen Sie das Debug-Skript aus, um Verbindungsprobleme zu diagnostizieren:
uv run python tests/debug_test.pyDies testet sowohl HTTP- als auch HTTPS-Verbindungen und liefert detaillierte Fehlerinformationen.
Beispiel für eine funktionierende Konfiguration
Hier ist ein funktionierendes Konfigurationsbeispiel zur Referenz:
# 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"OTRS-Webservice-Operationen
Ihr OTRS-Webservice sollte diese Operationen enthalten:
Operationsname | Controller | Beschreibung |
TicketCreate | Ticket::TicketCreate | Neue Tickets erstellen |
TicketGet | Ticket::TicketGet | Ticketdetails abrufen |
TicketSearch | Ticket::TicketSearch | Nach Tickets suchen |
TicketUpdate | Ticket::TicketUpdate | Vorhandene Tickets aktualisieren |
TicketHistoryGet | Ticket::TicketHistoryGet | Ticketverlauf abrufen |
Lizenz
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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