Get live training status and model tier for a specific dataset. model_tier: 'none' = deterministic only, 'base' = generic model, 'schema' = fully trained schema-specific model (best quality).
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Dataset ID from onboard_dataset or list_datasets. |
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 bears full responsibility. It explains the output (status and model tier) and the tier meanings, which is useful. However, it does not disclose any behavioral traits such as rate limits, idempotency, or whether it requires special permissions. The read-only nature is implied but not explicit.
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 two sentences, front-loading the purpose and adding a clarifying explanation for model_tier. Every sentence contributes meaningfully, with no redundancy or fluff.
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 description explains the return values (training status and model tier) and defines model_tier options. However, it does not elaborate on what 'live training status' includes (e.g., possible values like 'training', 'ready'), which slightly limits completeness. With no output schema, more detail would be beneficial, but it is still largely adequate.
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 covers 100% of the parameter with a clear description. The tool description does not add additional explanation for the parameter itself, so it meets the baseline. The description adds value for output values, not parameters.
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 states the tool retrieves live training status and model tier for a dataset. It explains model_tier meanings, aiding agent understanding. However, it does not explicitly distinguish from sibling get_* tools, which lowers it from a 5.
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 alternatives like get_data_schema or get_data_summary. The parameter description in the schema mentions using dataset_id from other tools, but the tool description itself lacks usage context or prerequisites.
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 resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.
