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Together AI MCP by usefulapi

List a fine-tuning job's events

together_list_fine_tune_events
Read-only

List the event log of one fine-tuning job (queued, started, checkpoint saved, epoch completed, errors). The first place to look when a job failed. Together: GET /fine-tunes/{id}/events.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fine_tune_idYesThe job id, e.g. ft-abc123.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true, so the safe-read profile is covered. The description adds the useful diagnostic framing ('first place to look when a job failed') but doesn't describe ordering, pagination, retention, or how many events to expect. With annotations covering safety, a 3 reflects added context without rich behavioral disclosure.

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?

Two tight sentences plus an endpoint hint. The purpose and diagnostic framing are front-loaded, and every sentence earns its place with no redundancy.

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

Completeness4/5

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

For a simple read-only list tool with annotations covering safety and a fully documented single parameter, the description is nearly complete. It tells the agent what it returns (event types) and when to reach for it. Missing only return ordering/volume details, which is minor for a diagnostic event log.

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?

Only one parameter, and the schema fully documents it (100% coverage, e.g. 'ft-abc123'). The description adds the endpoint path context (GET /fine-tunes/{id}/events), which corroborates the parameter's role. Baseline for a 0-param tool is 4; here the single param is already well described, and the description doesn't add format detail 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?

States a specific verb and resource: 'List the event log of one fine-tuning job.' The parenthetical enumerates the actual event types (queued, started, checkpoint saved, epoch completed, errors), which makes the purpose concrete and distinguishable from siblings like together_get_fine_tune or together_list_fine_tunes.

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

Usage Guidelines4/5

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

Explicitly gives a strong usage context: 'The first place to look when a job failed.' That's a clear when-to-use signal for a diagnostic scenario. It doesn't name an alternative tool or state when-not-to-use, which keeps it short of a 5.

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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