CareerProof MCP

atlas_update_custom_eval_model

Idempotent

Update a custom evaluation model's name, description, dimensions or weights. Free.

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": {
      "model_id": "<model_id>"
    },
    "name": "atlas_update_custom_eval_model"
  }
}

Input Schema

Name	Required	Description
`name`	No
`model_id`	Yes	From atlas_list_custom_eval_models
`description`	No
`inferred_weights`	No
`inferred_dimensions`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedNov 11, 2026

removedInput schema / properties / description / description
```
Removed value: -"Updated model description"
```
removedInput schema / properties / inferred_dimensions / description
```
Removed value: -"Updated evaluation dimensions array"
```
removedInput schema / properties / inferred_weights / description
```
Removed value: -"Updated dimension weights object"
```

changedInput schema / properties / model_id / description

Previous value: -"Model ID from atlas_create_custom_eval_model or atlas_list_custom_eval_models"New value: +"From atlas_list_custom_eval_models"

removedInput schema / properties / name / description
```
Removed value: -"Updated model name"
```

First observedMar 30, 2026

TDQS

B3.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=true and destructiveHint=false, and 'Update' is consistent with them. The description adds a useful cost signal ('Free') beyond the annotations, but omits key behavior such as whether omitted fields are preserved (partial update) and whether nested dimensions/weights are replaced wholesale or merged.

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?

Two very short sentences, front-loaded with the action and mutable fields. The fragment 'Free.' is terse but informative rather than filler; nothing is bloated.

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

Completeness2/5

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

This is a mutation tool with five parameters, nested objects, no output schema and only one required field, so partial-update semantics and the shape of inferred_dimensions/inferred_weights are essential. Neither is covered, leaving an agent unable to confidently construct the payload for the nested parameters.

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 only 20%, so the description must carry weight; it does name four of the five parameters (name, description, dimensions, weights), and model_id is documented in the schema as coming from atlas_list_custom_eval_models. The nested-object payloads (inferred_weights, inferred_dimensions) remain entirely undocumented in both places, leaving a real gap.

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

Purpose4/5

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

The description gives a specific verb and resource ('Update a custom evaluation model') and enumerates the mutable fields, which separates it from atlas_create_custom_eval_model, atlas_delete_custom_eval_model and atlas_get_custom_eval_model. However it uses the words 'dimensions' and 'weights' while the schema names them 'inferred_dimensions' and 'inferred_weights', so the field mapping isn't exact.

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

Usage Guidelines2/5

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

There is no guidance on when to update versus regenerate or infer a rubric (e.g. atlas_infer_custom_eval_rubric), and no prerequisites or exclusions. The only contextual hint is the trailing 'Free' cost note.

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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