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ultralytics-mcp

by amanharshx
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TypeScript
Remote

training_monitor

Read-only

Report a model's training status and progress for public or private Ultralytics projects, showing epoch counters, progress percentage, ETA, and optional trainResults history.

Instructions

Report a model's training status and progress (works for private and public projects). reportedCurrentEpoch, reportedProgressPercentage, and etaMs are the training job's own counters, passed through verbatim; after a run ends reportedCurrentEpoch can count a post-training evaluation as an epoch, so it may be one higher than the epochs actually trained, with reportedProgressPercentage inflated to match, and etaMs can stay nonzero. With no training job, progress is reported unavailable rather than inferred. resultRecordCount counts trainResults records, which are not epochs: a history can include a post-training evaluation record, duplicate or missing epoch numbers, and records out of epoch order. lastReportedMetrics is the last record in API order with its reported epoch, not necessarily the latest epoch. Model quality (best epoch, the model's reported metrics) belongs to model_metrics. timing.elapsedMs is wall-clock since model creation, evaluated at request time: it tracks elapsed run time while training is active, but for a finished model it reflects the model's age, not training duration. Billed training time is computeCost.durationMs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesModel ref by owner/project/model, ul:// URI, or slug (requires project).
projectNoProject ref required when model is given by slug.
history_last_nNoLimit the history to the last N records in API order (default 20). Truncates a long run to its tail; the response always reports the window it covers (record counts and the min/max reported epoch), so a flat tail is not mistaken for a converged run.
include_historyNoInclude the trainResults history (per-record epoch and metrics); omitted by default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.15
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / history_last_n / description
      Previous value: -"Limit the history curve to the most recent N epochs (default 20). Truncates a long run to its tail; the response always reports the epoch window it covers, so a flat tail is not mistaken for a converged run."New value: +"Limit the history to the last N records in API order (default 20). Truncates a long run to its tail; the response always reports the window it covers (record counts and the min/max reported epoch), so a flat tail is not mistaken for a converged run."
    • changedInput schema / properties / include_history / description
      Previous value: -"Include the epoch metrics history curve; omitted by default."New value: +"Include the trainResults history (per-record epoch and metrics); omitted by default."
  2. Changed2 schema fields changedv0.1.13
    • addedInput schema / properties / history_last_n / description
      Added value: +"Limit the history curve to the most recent N epochs (default 20). Truncates a long run to its tail; the response always reports the epoch window it covers, so a flat tail is not mistaken for a converged run."
    • addedInput schema / properties / include_history / description
      Added value: +"Include the epoch metrics history curve; omitted by default."
  3. Changed3 schema fields changedv0.1.12
    • removedInput schema / properties / include_metrics
      Removed value: -{
-  "type": "boolean"
-}
    • changedInput schema / properties / model / description
      Previous value: -"Model id, or slug when project is also provided."New value: +"Model ref by owner/project/model, ul:// URI, or slug (requires project)."
    • addedInput schema / properties / project / description
      Added value: +"Project ref required when model is given by slug."
  4. Changed4 schema fields changedv0.1.6
    • addedInput schema / properties / history_last_n
      Added value: +{
+  "exclusiveMinimum": 0,
+  "maximum": 9007199254740991,
+  "type": "integer"
+}
    • addedInput schema / properties / include_history
      Added value: +{
+  "type": "boolean"
+}
    • addedInput schema / properties / include_metrics
      Added value: +{
+  "type": "boolean"
+}
    • addedInput schema / properties / model / description
      Added value: +"Model id, or slug when project is also provided."
  5. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only cover the safety profile (readOnlyHint, openWorldHint, destructiveHint false); the description adds substantial behavioral context beyond that: counter pass-through semantics, the post-training-evaluation epoch inflation, nonzero etaMs after completion, the 'unavailable rather than inferred' behavior with no job, and record-ordering quirks. This is exactly the kind of caveat an agent cannot get from structured fields.

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

Conciseness4/5

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

Front-loaded with purpose and every sentence carries a non-obvious caveat, so little is wasted. However, the dense single paragraph mixes response-field semantics without any grouping, which makes it harder to scan than a structured list would be.

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?

There is no output schema, so the description must carry the full burden of explaining return values, and it does: it defines reportedCurrentEpoch, reportedProgressPercentage, etaMs, resultRecordCount, lastReportedMetrics, timing.elapsedMs, and computeCost.durationMs, including their edge cases. Nothing an agent needs to interpret a monitor response is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so all four parameters are already documented in the schema (including the history_last_n truncation window semantics). The description mostly elaborates on return fields rather than adding meaning to parameters, so the baseline 3 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?

States a specific verb and resource ('Report a model's training status and progress') and scopes it to private and public projects. It also implicitly separates itself from the sibling model_metrics by declaring that model quality belongs there, so an agent can distinguish the two without opening schemas.

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?

Provides clear context for use (monitoring a training job's progress and status) and explicitly routes the quality-evaluation need to model_metrics. It does not state when NOT to use it versus training_start/training_cancel, but the read-only monitoring intent is unambiguous.

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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