Botverse

Splice Video/Image Segments from URL

conform_splice_from_url

Concatenate an ordered list of video and/or still-image segments (all public HTTPS URLs) into one continuous, fresh-encoded output — the video 'splice' step. This is the general mechanism behind several common editorial moves, depending on where you put the image segment(s): a HEAD SLATE or BUMPER (image first — a title card or logo before the footage starts), a TAIL SLATE or END CARD (image last — a credits or contact card after the footage ends), or a MID-ROLL INSERT or CUTAWAY (image in the middle — a still dropped into the timeline between two shots). Use all-video segments to splice two or more real clips together back-to-back instead (no image involved at all). Each segment is either {type: 'video', url} or {type: 'image', url, duration_seconds} (duration_seconds is how long that still holds; required for image, ignored for video). Needs at least 2 segments (max 8) and at least one type 'video' segment — its resolution and frame rate become the output's defaults unless options override them. Every segment is scaled to fit and letterboxed (not stretched) if its aspect ratio differs, and any segment without audio (every image, or a silent video) gets a matching silent track synthesised so audio never drops out mid-timeline. Output formats: mp4, webm, mov_prores. Returns a job_id immediately. Poll get_job_status until 'complete', then get_download_url. RELIABILITY & FALLBACK: Botverse returns structured errors with a message. A bare 'HTTP 500' or 'tool call failed' with no message body is the MCP connector / sandbox layer between your client and Botverse — NOT a Botverse outage (the service is up if get_wallet_balance succeeds). If MCP calls keep failing, do NOT write your own ffmpeg / transcription / document-conversion code as a workaround — that wastes tokens and reinvents what Botverse does. Instead, tell the user to run the equivalent Botverse CLI from their own (non-sandboxed) machine, e.g. npx botverse transcribe <file> --to docx (also transcode and convert), then retry the MCP call.

Input Schema

TableJSON Schema

Name	Required	Description
`options`	No
`segments`	Yes	Ordered list of segments, played in array order. At least one must be type 'video'.
`output_format`	Yes	mp4 (H.264/AAC), webm (VP9/Opus), or mov_prores (ProRes 422).

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedSep 5, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: fresh encoding, letterboxing rather than stretching, synthesized silent audio tracks, resolution/framerate defaults from the first video segment, immediate job_id return, and the polling workflow. It also gives error-handling guidance for distinguishing Botverse errors from MCP connector failures. There is no contradiction with the annotations.

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 long but highly information-dense and organized: core behavior first, then use-case patterns, constraints, output behavior, async workflow, and reliability/fallback. The reliability paragraph is verbose with CLI examples, but still earns its place for preventing wasted retry/workaround efforts. Slight over-length keeps it from a 5.

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 tool with nested arrays, ordered segments, mixed media types, async job returns, and multiple output formats, the description is complete. It covers input constraints, segment behavior, output format options, default resolution/framerate behavior, audio handling, job polling, download flow, and error fallbacks. The presence of an output schema means return-value detail is not required, and nothing significant is missing.

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

Parameters5/5

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

Schema description coverage is 67%, and the description compensates richly: it explains segment type semantics, duration_seconds being required for images and ignored for videos, the ordered array behavior, minimum/maximum segment counts, the at-least-one-video rule, and how options default to the first video segment's resolution and framerate. These are critical semantic details not fully carried by the schema alone.

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 a specific verb and resource: concatenating an ordered list of video and/or still-image segments from public HTTPS URLs into one continuous, fresh-encoded output. It also names the common editorial use cases (head slate, tail slate, mid-roll insert, all-video splice), which distinguishes it from simple single-file conversion siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong usage context: when to use image-first/image-last/image-middle segments, when to use all-video segments, and required constraints (at least 2 segments, max 8, at least one video, public URLs). It does not explicitly name sibling alternatives or state when NOT to use this tool versus conform_from_url or conform_splice_media, so it falls just short of a 5.

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

A4.1/5.0

Disambiguation4/5

Each operation is split into clear source-specific variants (URL, uploaded file, inline content), and the descriptions go to great lengths to distinguish them. The only mild ambiguities are generic-sounding names like transcode_video versus transcode_from_url, and the similar get_job_status/get_workflow_status pair, but there is no true functional overlap.

Naming Consistency4/5

Most tools follow an imperative verb_noun pattern and use recurring suffixes like _from_url, _content, and _file, which creates a readable family structure. The pattern breaks slightly with uploaded-media variants named conform_media, transcode_video, and transcribe_media instead of a consistent _file or _uploaded suffix, and transcode_content is referenced in a description but missing from the actual tool list.

Tool Count4/5

17 tools is slightly above the ideal 3-15 range, but the server covers several related subdomains: document conversion, media transcode/transcribe/conform, job/workflow lifecycle, and wallet/billing. Given the need for URL, uploaded, and inline variants across multiple media types, the overall count is reasonable.

Completeness3/5

Core workflows are well covered: uploading, job submission, polling, and retrieving outputs all exist, and conversion has content/file/URL routes. However, get_upload_url explicitly tells agents to use transcode_content for inline media, but that tool does not exist, and there is no inline transcribe counterpart to convert_content, leaving a notable gap for sandboxed inline media jobs.

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