Bring Translation Management
to CrewAI
Learn how to connect Weblate to CrewAI and start using 32 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Weblate MCP Server?
Connect your Weblate instance to any AI agent to streamline your continuous localization and translation management through natural conversation.
What you can do
- Project & Component Management — List all projects, fetch component details, and explore translation files directly from the Weblate API.
- Language Insights — Retrieve detailed statistics for specific languages to track translation progress and identify missing strings.
- User & Group Administration — Manage user profiles, list contributions, and handle group roles or administrative permissions.
- Repository Operations — Perform critical repository actions like pulling updates or pushing translations to keep your version control in sync.
- Notification Control — List and manage user notification subscriptions to stay updated on translation changes.
How it works
- Subscribe to this server
- Enter your Weblate Instance URL and Personal API Token
- Start managing your localization projects from Claude, Cursor, or any MCP-compatible client
No more switching between your IDE and the Weblate dashboard to check translation status or user permissions. Your AI acts as a localization manager.
Who is this for?
- Localization Managers — quickly check project health, language coverage, and user contributions without manual reporting.
- Developers — trigger repository syncs and inspect component structures directly from the code editor.
- DevOps Engineers — automate user provisioning and group role assignments within the localization infrastructure.
Built-in capabilities (32)
Add team administrators to a group
Associate roles with a group
Create a new group
Create a new language definition
Create a new project
Create a new component in a project
Create a new role with specific permissions
Create a new Weblate user
Delete a user (marks inactive)
Get group details (roles, projects, components)
Get language details (plural formulas, aliases)
Global statistics for a language
Get project details
Get the URL to download all translations as a ZIP archive
Overall VCS status for the project
Get role details and permission codenames
Get Weblate API root entry point
Get detailed user information
List translations with user contributions
Get user translation statistics
List Weblate groups
List all languages
List components within a project
Manage project labels
Paginated statistics for all languages in a project
List all projects
List roles associated with the user
List user notification subscriptions
Requires management permissions or returns self. List Weblate users
Manage user notification subscriptions
Perform VCS operations (push, pull, commit, reset, cleanup)
Update user details
Why CrewAI?
When paired with CrewAI, Weblate becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Weblate tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
- —
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter and agents auto-discover every available tool at runtime - —
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
- —
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Weblate in CrewAI
Weblate and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Weblate to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Weblate in CrewAI
The Weblate MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 32 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Weblate for CrewAI
Every tool call from CrewAI to the Weblate MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I check the translation progress for a specific language code?
Yes! Use the get_language_statistics tool with the language code (e.g., 'fr'). The agent will return detailed metrics including translated, fuzzy, and failing strings.
Is it possible to trigger a Git pull or push from the AI?
Absolutely. Use the perform_repository_operation tool. You can specify the project and component along with the operation (like 'pull' or 'push') to sync with your remote repository.
Can I manage user access and view their contributions?
Yes. You can use list_users to see accounts, get_user_contributions to audit translation activity, and add_group_roles to manage permissions programmatically.
How does CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
Can different agents in the same crew use different MCP servers?
Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
What happens when an MCP tool call fails during a crew run?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
Can I run CrewAI crews on a schedule (cron)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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