Retrieve available AI vision models for analyzing images from URLs, files, or base64 data through the Vision MCP Server.
MIT
180,147 tools. Last updated 2026-06-06 01:32
"vision" matching MCP tools:
- Capture an accessibility snapshot of the current page to provide structured, queryable content without relying on screenshots or vision models.
- Retrieves enriched metadata for a fired Pythia Vision by ID: pattern ranges, failure profile, cooldown context, concurrent fires. Enables AI agents to size positions and compare historical failure modes.MIT
- Run the pdfhell adversarial-PDF benchmark to evaluate a vision model's robustness against malicious PDFs. Returns pass rate and per-family statistics.Apache 2.0
- Retrieve walk-forward validated pattern detections with accuracy stats, contract details, and live token sets for on-chain market intelligence.MIT
- Fetch the latest decoded video frame from a live Luxxon session as a JPEG, ready for direct input to a vision model. Retry if a 404 is returned during initial keyframe arrival.MIT
Matching MCP Servers
- AlicenseAqualityCmaintenanceHelps non-vision models extract and understand images and screenshots via OpenRouter multimodal models.Last updated2152MIT
- AlicenseAqualityCmaintenanceEnables image analysis and understanding using Vision Language Models through OpenAI-compatible APIs. Supports analyzing images from URLs or local files with custom prompts.Last updated12MIT
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- Retrieve workspace details: strategy (vision, non-negotiables, architecture principles), active products, and constitution rules to align teams on structured intent.MIT
- Cross-reference assembled board photos against a BOM file to verify component presence, polarity, orientation, and value. Review flagged components manually before accepting the first article.Apache 2.0
- Query ODEI's constitutional knowledge graph to explore structured domains, find specific entities, and understand connections between goals and execution.
- Retrieve recent Pythia Visions for any token with pattern breakdowns and statistics. Analyze historical vision data to inform trading decisions.MIT
- Returns the full game state visible to your team, including board dimensions, terrain, visible units with status, turn number, active player, and win-condition progress. Use at turn start to orient before making specific decisions.Apache 2.0
- Analyze a module's complete structure, dependencies, and functionality before making changes or writing documentation to prevent breaking existing systems.MIT
- Run a design system audit to check spec compliance for WCAG accessibility, token coverage, and naming conventions. Returns structured findings with pass/warn/fail status.MIT
- Get guidance on writing effective prompts for Veo video generation. Structure prompts for improved results using examples and tips.MIT
- Get guidance on structuring effective Midjourney prompts with parameters and examples to achieve desired image outcomes.MIT
- Analyze image content with quick summaries or structured 7-dimension analysis. Ask specific questions to extract text, objects, charts, and evidence from images.MIT
- Retrieve design documents like PRDs and CLAUDE.md files to understand code intent and verify implementation against original specifications.MIT
- Split large images or web page captures into optimally sized tiles for LLM vision analysis, reducing token costs by fetching only non-blank, relevant tiles.MIT
- Generate detailed descriptions of images using base64-encoded data. Ideal for uploaded images in chat conversations, providing accurate analysis via advanced vision APIs.MIT
- Render a preview of a TouchDesigner operator and generate a plain-text caption with color stats to verify the network is rendering correctly, using vision or histogram analysis.MIT