Veo 3.1 MCP Server
Enables high-quality AI video generation using Google's Veo 3.1 model for text-to-video, style-guided, and frame-interpolation tasks. It features token-efficient reference image handling, batch processing, and video extension capabilities with built-in cost estimation.
Caution. Specific findings reduced this grade. They are listed on the page. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.
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waimakers
Source: Glama
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The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.
These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.
upload_imageUpload an image to Google Files API for use as reference, first frame, or last frame in video generation. Returns a fileUri that can be reused for 48 hours. This is the most token-efficient way to pass images to video generation.
start_video_generationStart a Veo 3.1 video generation job. This returns an operation ID immediately - use get_video_job to poll for completion. Supports text-to-video, reference images (up to 3), and first/last frame interpolation.
get_video_jobCheck the status of a video generation job. Returns status and video URLs when complete. Videos are returned as download URLs - you can save them locally or share the links.
extend_videoExtend a Veo-generated video by additional seconds. Input video must be from a previous Veo generation (not an arbitrary video).
start_batch_video_generationStart multiple video generation jobs with controlled concurrency. Returns operation names for all jobs. Use this to generate multiple videos efficiently while respecting rate limits.
estimate_veo_costEstimate the cost in USD for a video generation request before starting it. Helps plan budgets and batch sizes.
Disclosed vulnerabilities in this server's declared npm dependencies (via OSV). Whether each is reachable depends on the installed versions.
Model Context Protocol (MCP) TypeScript SDK does not enable DNS rebinding protection by default
axios Vulnerable to Full Man-in-the-Middle via Prototype Pollution Gadget in `config.proxy`
axios Vulnerable to Credential Theft and Response Hijacking via Prototype Pollution Gadget in Config Merge
Axios is Vulnerable to Denial of Service via __proto__ Key in mergeConfig
Axios is vulnerable to DoS attack through lack of data size check
GEMINI_API_KEY"": "your_api_key_here"GOOGLE_CLOUD_LOCATIONGOOGLE_CLOUD_PROJECTTool annotations
No tools have read-only/destructive annotations
Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.
All four hints declared on every tool
6/6 tools missing one or more hints — upload_image (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); start_video_generation (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_video_job (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +3 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.
For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.
License file
No license file
Add a LICENSE file (MIT, Apache-2.0, etc.).
Tests exist
No test files found
Add tests that exercise each declared tool.
Production dependencies are patched
0 critical, 14 high severity in production deps — @modelcontextprotocol/sdk@1.0.0 (high), axios@1.6.0 (high)
Run npm audit fix, or upgrade the affected packages to a non-vulnerable version.
Domain consistency
npm scope @yourorg doesn't match GitHub owner waimakers
Use the same org name across GitHub, npm, and your homepage so users can verify the publisher.
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