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amanharshx

ultralytics-mcp

by amanharshx
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TypeScript
Remote

model_plots

Read-only

Report a model's evaluation plots (PR, F1, precision, recall, confusion matrix) by listing each plot's field shapes, or fetch one plot's full data by type.

Instructions

Report a model's evaluation plots (per-class pr_curve, f1_curve, precision_curve, recall_curve, confusion_matrix), which model_metrics and training_monitor do not surface. By default lists each plot's type and the shape of its fields (array lengths only, never the values) since one pr_curve alone can carry thousands of numbers on a multi-class model; pass type to get that one plot's data back exactly as the platform returned it, unmodified. Field shapes vary by type: pr_curve/f1_curve/precision_curve/recall_curve carry x/y (and pr_curve additionally ap); confusion_matrix carries a matrix field instead, not x/y/ap. Plot presence does not track training history: a model can have plots with no trainResults, or (rarely) plots: [] on an otherwise completed model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoReturn this one plot's full data unmodified (e.g. pr_curve, confusion_matrix). Omit to list what's available.
modelYesModel ref by owner/project/model, ul:// URI, or slug (requires project).
projectNoProject ref required when model is given by slug.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.15
    • addedInput schema / additionalProperties
      Added value: +false
  2. Addedv0.1.12

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover the read-only safety profile, and the description goes well beyond that: it discloses the default shape-only payload (never values), the exact-as-returned behavior when type is passed, per-type field makeup, and the non-correlation between plot presence and trainResults. That is information an agent cannot get from annotations or schema.

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 the purpose then the default/override behavior, and every sentence carries distinct payload-related information. It is dense and long-sentenced, slightly straining the 'just the essentials' bar, but nothing is padding.

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?

With no output schema, the description supplies the return-shape detail an agent needs: which fields appear per plot type, that values are withheld by default, and the edge case of plots existing independently of training history. Complete for this tool without an output schema.

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 is already 100%, so the baseline is 3; the description earns above it by explaining what type actually changes in the response (full unmodified data vs. a listing) and enumerating valid plot types along with the fields each returns.

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?

Specific verb (report) plus resource (a model's evaluation plots) with the exact set of artifacts named. It explicitly distinguishes itself from model_metrics and training_monitor, so an agent can route without opening a sibling schema.

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?

States the decision rule plainly: omit type to list availability, pass type to get one plot's data unmodified, and the alternative tools do not surface these plots. No inference required.

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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