Localizable XStrings MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_MODEL | No | OpenAI model for translations | gpt-4o-mini |
| OPENAI_API_KEY | Yes | Your OpenAI API key | |
| OPENAI_BASE_URL | No | Custom API base URL | |
| TRANSLATION_CHUNK_SIZE | No | Strings per API request | 50 |
| TRANSLATION_TEMPERATURE | No | Model creativity (0.0-1.0) | 0.3 |
| TRANSLATION_RATE_LIMIT_DELAY | No | Delay between requests (seconds) | 1.0 |
| TRANSLATION_MAX_CONCURRENT_CHUNKS | No | Max concurrent requests | 2 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_languages_toolC | |
| get_keys_toolC | |
| get_base_strings_toolB | |
| translate_toolC | |
| apply_toolC | |
| apply_missing_toolA | |
| translate_key_toolB | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
There is significant overlap between translate_tool, apply_tool, and translate_key_tool, as all involve translating strings, which could confuse an agent. However, apply_missing_tool is distinct by focusing only on missing translations, and get_* tools are clearly separate for retrieval purposes.
Most tools follow a consistent verb_noun pattern (e.g., get_keys_tool, translate_key_tool), but apply_missing_tool and apply_tool deviate slightly by omitting the noun, which is a minor inconsistency. Overall, the naming is readable and predictable.
With 7 tools, the count is well-scoped for managing xcstrings files, covering retrieval, translation, and application operations. Each tool serves a distinct role in the localization workflow, making the set appropriately sized.
The toolset covers key aspects like retrieving base strings, keys, and languages, plus translation and application functions. A minor gap is the lack of tools for updating or deleting translations, but core operations are present, allowing agents to handle most localization tasks.