Botverse

Convert Inline Content

convert_content

Convert a document inline — pass the content directly as a string (or base64 for binary inputs like .docx). PREFERRED route for documents, and the one to use in sandboxed agent environments (claude.ai, Claude Desktop, Cursor): it runs entirely server-side, so it never needs the S3 upload those sandboxes block. Limit: up to 4 MB of content — already huge (a 500-page book is ~1 MB of text). For anything larger, use convert_from_url with a public URL. Supported inputs: md, html, rst, txt (plain text), docx (base64). Supported outputs: docx (Word), pdf, html, txt, md, rst, xlsx. Returns a job_id — poll get_job_status until 'complete', then get_output_content (inline bytes, sandbox-safe) or get_download_url (S3 link). Flat fee $0.05 per file. TIP: if you have shell access and are NOT sandboxed (e.g. a local coding agent), the botverse CLI (npx botverse convert <file> --to <fmt>) is faster for local files — it streams from disk instead of re-emitting the content through the model.

Input Schema

TableJSON Schema

Name	Required	Description
`content`	Yes	The file content as a plain text string (for md, html, rst, txt) or base64-encoded bytes (for docx).
`encoding`	No	Encoding of the content field. Defaults to "text". Use "base64" for binary inputs like .docx.
`input_format`	Yes	Source format of the content.
`output_format`	Yes	Target format: docx \| html \| txt \| md \| rst \| pdf \| xlsx

Output Schema

TableJSON Schema

Name	Required	Description
`job_id`	Yes	Unique identifier for this job. Pass to get_job_status and get_download_url.
`status`	Yes	Initial job state — always queued or processing immediately after submission.
`estimated_seconds`	No	Rough estimated processing time in seconds. Actual time may vary.

TDQS

A4.8/5.0

Behavior5/5

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

The description discloses significant behavioral details beyond the annotations: it runs server-side (avoiding S3 uploads), has a 4MB limit, returns a job_id requiring polling, and charges a $0.05 fee. These are crucial operational traits that annotations do not convey, enriching the agent's understanding of side effects and cost.

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?

The description is a single, dense paragraph but is logically structured: core action, preference, limits, formats, return flow, cost, and tip. While slightly lengthy, every sentence carries essential information for this complex tool, and it is front-loaded with the primary purpose.

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?

Given the tool's complexity (4 params, job polling, sandbox implications, cost), the description is remarkably thorough. It covers input methods, size limits, alternatives, output formats, the polling workflow, pricing, and environment-specific recommendations. The output schema presumably handles return details, so the description is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema already has 100% coverage, the description adds valuable semantic context: the 4MB limit for content and the explicit requirement to use base64 for docx. It reinforces the encoding parameter's purpose and gives a practical constraint not present in the schema.

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 clearly states the tool's function: 'Convert a document inline — pass the content directly as a string'. It distinguishes itself from the sibling convert_from_url by explicitly naming it as the alternative for larger content, making it unmistakable which tool to use for inline content.

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?

The description gives explicit usage context: 'PREFERRED route for documents... use in sandboxed agent environments' and 'For anything larger, use convert_from_url with a public URL.' It also provides a CLI alternative for local, non-sandboxed environments, covering both when-to-use and when-not-to-use.

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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