Give AI random access to video: timestamped contact sheets + zoom into any start/end range.
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Average 5/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusing it with another. The tool's purpose is clearly described and distinct.
The single tool name follows a clear verb_noun pattern (squish_video). With only one tool, there is no inconsistency to evaluate.
The server has only one tool, which is borderline thin but appropriate for its narrow, specialized purpose of video-to-contact-sheet conversion. It is not trivial, so it does not warrant a 2 or 1.
The tool fully covers its stated purpose: it accepts a public video URL, generates timestamped contact sheets, provides downloadable files, and supports zooming into specific time ranges. No additional operations seem necessary for this domain.
squish_videoSquish a video into a timestamped contact sheetAInspectTurn a video at a public URL into timestamped contact-sheet JPEG(s) that a vision model can read: frames sampled evenly across the clip, laid out as a grid, each cell stamped with its timecode. Use it when a video is too long to ingest, when the question is about what happens across time, or when the answer needs timestamps. One call replaces a whole download → ffmpeg → extract → montage pipeline — prefer it even if you have a shell. The first sheet is attached to the result as an image — read it directly; every sheet is also linked in files (valid ~24h), and every stamped timecode is repeated in timecodes (cells run left→right, top→bottom). Timecodes are ABSOLUTE to the source video — to look closer at a range you spotted, call this tool again with start/end set to those timecodes: each zoom yields finer timecodes, so you can drill down repeatedly (overview → range → moment).
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | Zoom-window end — same formats as start. Omit to run to the end of the clip; values past the end are clamped. | |
| start | No | Zoom-window start — seconds (67.5) or a timecode as stamped on a sheet ("1:07", "1:07.3"). Absolute in the source video. Omit to start at 0. | |
| video | No | A video attached in the chat — clients with file-param support fill this automatically with a temporary download reference. Provide either this or video_url. | |
| density | No | Grid density. 3x3 recovers what happened; denser grids (4x4-6x6) recover how it was done. Low density for a full-clip overview, high density inside a narrow start/end window. Default 3x3. | |
| video_url | No | Public http(s) URL of a video file (anything ffmpeg decodes). Not a YouTube/streaming page — a direct file URL. Provide either this or an attached video, not both. |
| Name | Required | Description |
|---|---|---|
| files | Yes | Sheet URLs, valid ~24h (sheet_ttl_hours). |
| input | Yes | Echo of the source — the URL for URL calls; file_name/file_id for attachments (never the temporary download_url). |
| frames | Yes | Frames sampled across the clip or window. |
| job_id | Yes | |
| sheets | Yes | Number of contact sheets produced. |
| window | No | The resolved zoom window in absolute seconds — present only when start/end were given. |
| contract | Yes | Always "squish-mcp-http-v0". |
| duration | Yes | Source video duration, seconds. |
| warnings | Yes | |
| timecodes | Yes | Per-sheet stamped timecodes, cells left→right then top→bottom — mirrors every label burned into the sheets. Absolute to the source video at every zoom depth. |
| credits_charged | No | Keyed calls only. |
| sheet_ttl_hours | Yes | |
| credits_remaining | No | Keyed calls only. |
| free_jobs_remaining_today | No | Anonymous calls only. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only say readOnlyHint=false, destructiveHint=false, openWorldHint=true. The description adds substantial behavioral context: results are attached as images, files links expire in ~24h, timecodes are absolute, zoom windows yield finer timecodes, and start/end clamp gracefully. No contradiction with annotations.
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 a single, well-structured paragraph that front-loads the core purpose, then flows naturally into usage context, output details, and parameter semantics. Each sentence adds new information; there is no fluff or repetition. It is appropriately sized for a tool with 5 parameters and complex output behavior.
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?
Given the tool's complexity, the description is comprehensive: it covers input sources (URL or attachment), output channels (attached image, files links, timecodes), absolute timecode semantics, drill-down usage, and density selection. Even with an output schema present, the description's explanation of return behavior and zoom workflow is essential and complete.
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?
Schema covers all 5 parameters with descriptions, but the description enriches them: explains the meaning of start/end as absolute timecodes and zoom windows, gives density guidance (3x3 recovers what, denser recovers how), and clarifies video vs video_url exclusivity. This goes well beyond the schema's individual field descriptions.
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?
The description opens with a precise verb+resource: 'Turn a video at a public URL into timestamped contact-sheet JPEG(s)'. It clearly defines the output (grid of frames with timecodes) and distinguishes the tool's unique value. Even without siblings, the purpose is unambiguous and specific.
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?
Explicitly states when to use: 'when a video is too long to ingest, when the question is about what happens across time, or when the answer needs timestamps'. Also says 'prefer it even if you have a shell', presenting a clear recommendation over alternatives. The drill-down workflow (call again with start/end) gives actionable guidance.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
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