fireworks_list_datasetsList datasets in the account. Control-plane: GET /v1/accounts/{account_id}/datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| pageSize | No | Page size (max 200). | |
| pageToken | No | Opaque page token from a previous response's nextPageToken. | |
| account_id | No | Fireworks account id. Overrides FIREWORKS_ACCOUNT_ID for this call. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is established. The description adds that this is a control-plane GET request, which is consistent. However, it does not disclose pagination behavior or rate limits beyond the schema's pageSize maximum. Given the annotations, the description adds minimal additional behavioral context.
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 extremely concise: two sentences with no fluff. It front-loads the purpose and includes the API endpoint for reference. Every word earns its place.
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 list tool with 3 parameters and no output schema, the description is incomplete. It doesn't mention the response structure (e.g., returns a list of dataset objects with nextPageToken). While the annotations and schema cover safety and parameters, the lack of output description leaves the agent without full context.
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?
Input schema has 100% description coverage, so parameters are fully documented. The description does not add any extra meaning beyond what the schema provides. Baseline 3 is appropriate since the schema already carries the burden.
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?
Description clearly states 'List datasets in the account' with a specific verb and resource. It also provides the API endpoint, leaving no ambiguity. This distinguishes it from sibling tools like fireworks_get_dataset (single dataset) and fireworks_create_dataset (create).
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?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention when to use list vs get_dataset or how pagination might affect usage. The description is minimal and lacks decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct resource and action combination (e.g., create_dataset vs delete_deployment vs list_models), with no overlapping purposes. Descriptions clearly differentiate each tool.
All tools follow a consistent 'fireworks_verb_noun' pattern, with verbs like create, delete, get, list and singular or plural nouns as appropriate. No mixing of conventions.
14 tools cover the main resources of the Fireworks platform (accounts, datasets, deployments, fine-tuning jobs, models, batch inference, users). The scope is appropriate and not overwhelming.
The tool set is heavily read-oriented with only one creation tool (create_dataset) and one deletion tool (delete_deployment). Missing create for deployments, fine-tuning jobs, models; missing update and delete for most resources. Gaps would hinder full workflow automation.