Algenta MCP Server

get_dataset_status

Read-onlyIdempotent

Get the live training status and model tier of one dataset: whether semantic training is still running or the dataset is ready, and which model serves queries — model_tier 'none' = deterministic fallback only, 'base' = generic model, 'schema' = fully trained schema-specific model (best quality). Poll this after onboard_dataset until training completes, and use retrain_dataset after schema or alias changes. Read-only; an unknown dataset_id fails with not_found. Also returns name, column_count, source_names, and updated_at.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>"
    },
    "name": "get_dataset_status"
  }
}

Input Schema

Name	Required	Description	Default
`dataset_id`	Yes	Dataset ID from onboard_dataset or list_datasets.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 12, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations: unknown dataset_id fails with not_found, the meaning of model_tier values, and the returned fields. This fully discloses expected behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence earns its place: purpose, model-tier meanings, usage sequencing, error behavior, and returned fields. It is compact but information-dense, with the primary purpose front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read-only status tool with no output schema, the description is complete: it covers return fields, model-tier interpretation, polling guidance, error behavior, and distinguishes from related actions. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage for dataset_id is 100%, so the schema already documents the parameter. The description adds value by explaining that an unknown dataset_id produces a not_found error and that the parameter references a dataset from onboard_dataset, reinforcing usage semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Get the live training status and model tier of one dataset') and distinguishes it from sibling status tools by focusing on semantic training and model tier. It clearly says what the tool does without tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context: poll after onboard_dataset until training completes, and use retrain_dataset after schema or alias changes. This tells an agent when to call this tool and when to use an alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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