List batch jobs
together_list_batchesList batch inference jobs with status, progress, model and input/output/error file ids. Together: GET /batches.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
together_list_batchesList batch inference jobs with status, progress, model and input/output/error file ids. Together: GET /batches.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already tells the agent this is a safe read, so the description carries a lighter burden. It usefully discloses the shape of the returned records (status, progress, model, file ids), but says nothing about pagination, result limits, or ordering — meaningful gaps for a list endpoint.
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?
Two tightly packed sentences with no waste; the enumeration of returned fields comes before the API endpoint mapping, so the most useful information is front-loaded.
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?
With no output schema, the description does the work of naming the key fields on returned batch records, which is enough for an agent to use the result. It falls short only on operational details like pagination and volume, which are not covered anywhere else.
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?
The tool takes zero parameters, so per the baseline a 4 applies. There are no argument semantics to clarify, and the description correctly implies the call is unfiltered.
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?
States a specific verb and resource ('List batch inference jobs') and enumerates the notable fields returned (status, progress, model, file ids). It is clearly distinguishable from together_get_batch and together_list_fine_tunes, though it never names a sibling explicitly.
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?
Usage is only implied: the agent can infer this is the tool for enumerating existing batches rather than fetching one (together_get_batch) or creating one (together_create_batch). There is no explicit when-to-use statement, no exclusions, and no note about whether it returns all batches or a page.
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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