Get the status of a batch job (e.g. machine translation) by its ID. Use to poll for completion.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | ID of the batch job | |
| projectId | No | ID of the project (required for PAT, auto-resolved for PAK) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Poll for completion' implies a non-mutating, repeated-read behavior, but the description does not disclose return format, status values, or any potential side effects. This is adequate but minimal.
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 exactly two sentences, front-loaded with the core action, and every word contributes value. No wasted or redundant phrasing.
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 (2 params, no output schema), the description covers the main purpose and usage. However, it omits return value details or hints about possible statuses, which could be necessary for an agent to interpret the response correctly. This leaves a minor gap in 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 provides 100% coverage with descriptions for both jobId and projectId. The description adds no additional semantics beyond what the schema already states, so the baseline score of 3 is appropriate.
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 ('Get the status') and resource ('batch job') with an identifying criterion ('by its ID'). It also provides a concrete example ('e.g. machine translation'), clearly distinguishing it from sibling tools like get_translations or machine_translate.
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 phrase 'Use to poll for completion' explicitly explains when this tool should be used. While it does not mention alternatives or exclusions, the usage context is clear and relevant for a polling scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct entities and actions, but a few pairs like create_keys/set_translation and list_keys/search_keys have overlapping functionality. The descriptions clearly differentiate their use cases, so ambiguity is limited.
All tools follow a consistent snake_case verb_noun pattern (create_, delete_, get_, list_, set_, etc.), making the API predictable. The only slight deviation is machine_translate, but it still reads as a single action verb.
With 22 tools, the server covers many aspects of translation management, but the count is slightly above the ideal range. Each tool has a clear purpose, though some could potentially be consolidated without loss of functionality.
The core key and translation lifecycle is well covered, but there are notable gaps: languages only support create and list (no update/delete), tags can be added but not removed, and projects lack get/update/delete operations. These omissions may require workarounds.
