Botverse

Get Output Content (Inline)

get_output_content

Read-onlyIdempotent

Return the finished output of a completed job INLINE as base64 — no S3 download. Use this in sandboxed agent environments (claude.ai, Claude Desktop, Cursor) where fetching a get_download_url link is blocked; it delivers the bytes over the same tool channel that always works. Call after get_job_status returns 'complete'. Limited to 4 MB outputs — for larger files use get_download_url (and a non-sandboxed environment, or add the S3 host to your egress allowlist).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	Yes	Job ID from any convert or transcode job.

Output Schema

TableJSON Schema

Name	Required	Description
`mime`	Yes	MIME type of the output, e.g. application/pdf.
`format`	Yes	Output file extension, e.g. pdf or docx.
`encoding`	No	Always "base64".
`size_bytes`	Yes	Decoded file size in bytes.
`content_base64`	Yes	The output file bytes, base64-encoded. Decode and write to disk.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds genuinely useful behavioral context: outputs are base64-encoded, delivered inline over a channel that 'always works', capped at 4 MB, and only available for completed jobs. This exceeds what annotations alone convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with the core purpose front-loaded, followed by use context, then size limit and alternative. Every sentence adds distinct value, with no redundant wording or unnecessary elaboration.

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?

For a single-parameter tool with rich annotations and an output schema, the description covers purpose, timing, environment restrictions, size cap, and fallback tool. Nothing essential is missing for an agent to select and invoke it correctly.

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?

The only parameter, job_id, has full schema description coverage ('Job ID from any convert or transcode job'). The description does not add extra parameter-specific details, but given the schema already documents the parameter well, the baseline score of 3 applies.

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?

The description opens with a specific verb and resource: 'Return the finished output of a completed job INLINE as base64 — no S3 download.' It clearly distinguishes this tool from get_download_url by highlighting the inline base64 delivery mechanism and the completed-job precondition.

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?

It explicitly states when to use this tool (sandboxed agent environments where get_download_url is blocked), when to call it (after get_job_status returns 'complete'), and provides a concrete alternative for larger outputs (get_download_url). This gives the agent unambiguous selection criteria.

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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TDQS

A4/5.0

Disambiguation4/5

Tools are largely distinguishable by their action and input source (content/file/url), but the multiple convert_* and transcode/transcribe variants overlap in purpose, and get_job_status vs get_workflow_status could cause confusion. Descriptions are thorough enough to guide selection, but the boundaries between similar actions are not always crisp.

Naming Consistency4/5

All names use snake_case with a verb-noun pattern (get_, convert_, transcode_, transcribe_, submit_, cancel_), which is consistent. However, the variant naming is not perfectly parallel: convert_content/file/from_url use a source suffix, whereas transcode_video vs transcode_from_url and transcribe_media vs transcribe_from_url mix output type and source, creating minor inconsistency.

Tool Count4/5

At 15 tools, the server is at the upper end of a well-scoped set. Each tool supports a distinct input/output route or workflow function, but the count is slightly heavy due to multiple variants of similar operations. It remains appropriate for the breadth of conversion, transcoding, transcription, workflow, and wallet features.

Completeness3/5

The core job lifecycle (submit, poll, retrieve output) is well covered, and workflows have submit/status/cancel. However, get_upload_url references a transcode_content tool that does not exist in the toolset, and there is no way to cancel a single job (only full workflows). This leaves gaps for inline media transcoding in sandboxed environments and granular job control.

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