Skip to main content
Glama
Sign In

Algenta MCP Server

by io.github.thyn-ai
OverviewDiscussionsAdmin

retrain_dataset

Re-trigger background semantic training for one dataset and return immediately with status and a confirmation message — the build runs asynchronously, so poll get_dataset_status until model_tier reaches 'schema'. Use after schema changes, alias updates, or to force a fresh model build. epochs (default 80, range 5-500) controls training length. An unknown dataset_id fails with not_found; a dataset whose training backend is unavailable fails with semantic_training_unavailable.

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": "retrain_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
epochsNoTraining epochs, 5-500; defaults to 80.
dataset_idYesDataset ID from onboard_dataset or list_datasets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / dataset_id / description
      Added value: +"Dataset ID from onboard_dataset or list_datasets."
    • addedInput schema / properties / epochs / description
      Added value: +"Training epochs, 5-500; defaults to 80."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description discloses the async nature (build runs asynchronously, returns immediately), the need to poll, and two specific error conditions (not_found and semantic_training_unavailable). These go well beyond the annotations, which only indicate readOnlyHint=false etc. The agent learns exactly what to expect and how to handle failures.

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?

The description is a tight 3-sentence block. The main action and async behavior are front-loaded, followed by usage guidance, parameter note, and error cases. Every sentence earns its place; there is no filler or repetition of schema text.

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?

Despite lacking an output schema, the description tells the agent what to expect (status and confirmation message) and how to obtain the final result (poll get_dataset_status). It covers the asynchronous workflow, parameter purpose, and failure modes. For a mutation tool of this complexity, nothing essential 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 description coverage is 100%, so the schema already documents both parameters. The description adds that epochs controls training length (and defaults/range, though those are also in schema) and clarifies that dataset_id comes from onboard_dataset or list_datasets, giving provenance. It does not deeply explain format or semantics beyond that, so a 4 is appropriate.

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 opens with a clear verb-resource pairing — 'Re-trigger background semantic training for one dataset' — and specifies the immediate return behavior. It also names the related tool get_dataset_status, which distinguishes this tool as the trigger versus the status poller. The stated use cases (schema changes, alias updates, force rebuild) further pin down its purpose.

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?

Explicitly states when to use: 'after schema changes, alias updates, or to force a fresh model build'. It also tells the agent exactly what to do after calling (poll get_dataset_status until model_tier reaches 'schema'), effectively providing a follow-up action and an alternative tool. This leaves no ambiguity about the workflow.

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

All Algenta MCP Server toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources