get_contribution_groupsRetrieve the stored contribution groups for a model, including legacy dashboard-saved configs served verbatim.
Read the stored contribution groups for a model (#436). Legacy dashboard-saved configs are served verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| model_hash | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.5.0Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": true,
+ "title": "get_contribution_groupsDictOutput",
+ "type": "object"
+}v0.3.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is disclosed. The description adds 'Legacy dashboard-saved configs are served verbatim,' which is a behavioral nuance beyond the annotations, but it is terse and cryptic ('#436') and does not explain implications like formatting or transformation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and front-loads the purpose, which is good. However, the second sentence is cryptic and not well structured; it references '#436' and a legacy behavior without context, making it less clear and slightly wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool with one parameter and an output schema, the description is minimal. It omits any explanation of the model_hash parameter and does not mention potential error cases or prerequisites. The cryptic legacy note adds confusion rather than completeness. The output schema exists, so return values are not required, but parameter description and edge cases are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the model_hash parameter at all. The parameter is named model_hash, which is somewhat self-explanatory, but the description adds no meaning about its format, requiredness, or how to obtain it. With zero coverage, the description must compensate, and it does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads stored contribution groups for a model, using the verb 'Read' and naming the resource. It distinguishes from the sibling set_contribution_groups, though it does not explicitly name that sibling. The reference to '#436' is cryptic and not helpful.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (reading contribution groups) but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It does not mention that set_contribution_groups is the write counterpart or any conditions that would route an agent here.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.