Xpersona

Crawler Summary

fal.ai Image Generation Server answer-first brief

MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. ## When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators needing unique visuals for blogs, presentations, or social media. - AI researchers and tinkerers experimenting with the latest fal.ai models. - Automating workflows that require programmatic image generation via MCP. Key features: - Supports any valid fal.ai model and all major image parameters. - Works out of the box with Node.js and a fal.ai API key. - Saves images locally with accessible file paths. - Simple configuration and robust error handling. ## Quick Start 1. Requirements: Node.js 18+, fal.ai API key 2. Configure MCP: { "mcpServers": { "fal-ai-image": { "command": "npx", "args": ["-y", "mcp-fal-ai-image"], "env": { "FAL_KEY": "YOUR-FAL-AI-API-KEY" } } } } 3. Run: Use the generate-image tool from your IDE. 💡 Typical Workflow: Describe the image you want (e.g., “generate a landscape with flying cars using model fal-ai/kolors, 2 images, landscape_16_9”) and get instant results in your IDE. ### 🗨️ Example Prompts - generate an image of a red apple - generate an image of a red apple using model fal-ai/kolors - generate 3 images of a glowing red apple in a futuristic city using model fal-ai/recraft-v3, square_hd, 40 inference steps, guidance scale 4.0, safety checker on Supported parameters: prompt, model ID (any fal.ai model), number of images, image size, inference steps, guidance scale, safety checker. Images are saved locally; file paths are shown in the response. For model IDs, see fal.ai/models. ## Troubleshooting - FAL_KEY environment variable is not set: Set your fal.ai API key as above. - npx not found: Install Node.js 18+ and npm. Advanced: Example MCP Request/Response Request: { "tool": "generate-image", "args": { "prompt": "A futuristic cityscape at sunset", "model": "fal-ai/kolors" } } Example response: { "images": [ { "url": "file:///path/to/generated_image1.png" }, { "url": "file:///path/to/generated_image2.png" } ] } ## 📁 Image Output Directory Generated images are saved to your local system: - By default: ~/Downloads/fal_ai (on Linux/macOS; uses XDG standard if available) - Custom location: Set the environment variable FAL_IMAGES_OUTPUT_DIR to your desired folder. Images will be saved in /fal_ai. The full file path for each image is included in the tool's response. ## ⚠️ Error Handling & Troubleshooting - If you specify a model ID that is not supported by fal.ai, you will receive an error from the backend. Double-check for typos or visit fal.ai/models to confirm the model ID. - For the latest list of models and their capabilities, refer to the fal.ai model catalog or API docs. - For other errors, consult your MCP client logs or open an issue on GitHub. ## 🤝 Contributing Contributions and suggestions are welcome! Please open issues or pull requests on GitHub. ## 🔒 Security - Your API key is only used locally to authenticate with fal.ai. - No user data is stored or transmitted except as required by fal.ai API. ## 🛡 License MIT License © 2025 Madhusudan Kulkarni MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

fal.ai Image Generation Server is best for general automation workflows where MCP compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, Smithery, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Agent DossierSmitherySafety: 86/100

fal.ai Image Generation Server

MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. ## When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators needing unique visuals for blogs, presentations, or social media. - AI researchers and tinkerers experimenting with the latest fal.ai models. - Automating workflows that require programmatic image generation via MCP. Key features: - Supports any valid fal.ai model and all major image parameters. - Works out of the box with Node.js and a fal.ai API key. - Saves images locally with accessible file paths. - Simple configuration and robust error handling. ## Quick Start 1. Requirements: Node.js 18+, fal.ai API key 2. Configure MCP: { "mcpServers": { "fal-ai-image": { "command": "npx", "args": ["-y", "mcp-fal-ai-image"], "env": { "FAL_KEY": "YOUR-FAL-AI-API-KEY" } } } } 3. Run: Use the generate-image tool from your IDE. 💡 Typical Workflow: Describe the image you want (e.g., “generate a landscape with flying cars using model fal-ai/kolors, 2 images, landscape_16_9”) and get instant results in your IDE. ### 🗨️ Example Prompts - generate an image of a red apple - generate an image of a red apple using model fal-ai/kolors - generate 3 images of a glowing red apple in a futuristic city using model fal-ai/recraft-v3, square_hd, 40 inference steps, guidance scale 4.0, safety checker on Supported parameters: prompt, model ID (any fal.ai model), number of images, image size, inference steps, guidance scale, safety checker. Images are saved locally; file paths are shown in the response. For model IDs, see fal.ai/models. ## Troubleshooting - FAL_KEY environment variable is not set: Set your fal.ai API key as above. - npx not found: Install Node.js 18+ and npm. Advanced: Example MCP Request/Response Request: { "tool": "generate-image", "args": { "prompt": "A futuristic cityscape at sunset", "model": "fal-ai/kolors" } } Example response: { "images": [ { "url": "file:///path/to/generated_image1.png" }, { "url": "file:///path/to/generated_image2.png" } ] } ## 📁 Image Output Directory Generated images are saved to your local system: - By default: ~/Downloads/fal_ai (on Linux/macOS; uses XDG standard if available) - Custom location: Set the environment variable FAL_IMAGES_OUTPUT_DIR to your desired folder. Images will be saved in /fal_ai. The full file path for each image is included in the tool's response. ## ⚠️ Error Handling & Troubleshooting - If you specify a model ID that is not supported by fal.ai, you will receive an error from the backend. Double-check for typos or visit fal.ai/models to confirm the model ID. - For the latest list of models and their capabilities, refer to the fal.ai model catalog or API docs. - For other errors, consult your MCP client logs or open an issue on GitHub. ## 🤝 Contributing Contributions and suggestions are welcome! Please open issues or pull requests on GitHub. ## 🔒 Security - Your API key is only used locally to authenticate with fal.ai. - No user data is stored or transmitted except as required by fal.ai API. ## 🛡 License MIT License © 2025 Madhusudan Kulkarni MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators

MCPself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Trust evidence available

Trust score

Unknown

Compatibility

MCP

Freshness

Apr 15, 2026

Vendor

Madhusudan Kulkarni

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

View Source

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Madhusudan Kulkarni

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

MCP

contractmedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredSmithery

Extracted files

0

Examples

0

Snippets

0

Languages

Unknown

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredSmithery

Docs source

Smithery

Editorial quality

ready

MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. ## When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators needing unique visuals for blogs, presentations, or social media. - AI researchers and tinkerers experimenting with the latest fal.ai models. - Automating workflows that require programmatic image generation via MCP. Key features: - Supports any valid fal.ai model and all major image parameters. - Works out of the box with Node.js and a fal.ai API key. - Saves images locally with accessible file paths. - Simple configuration and robust error handling. ## Quick Start 1. Requirements: Node.js 18+, fal.ai API key 2. Configure MCP: { "mcpServers": { "fal-ai-image": { "command": "npx", "args": ["-y", "mcp-fal-ai-image"], "env": { "FAL_KEY": "YOUR-FAL-AI-API-KEY" } } } } 3. Run: Use the generate-image tool from your IDE. 💡 Typical Workflow: Describe the image you want (e.g., “generate a landscape with flying cars using model fal-ai/kolors, 2 images, landscape_16_9”) and get instant results in your IDE. ### 🗨️ Example Prompts - generate an image of a red apple - generate an image of a red apple using model fal-ai/kolors - generate 3 images of a glowing red apple in a futuristic city using model fal-ai/recraft-v3, square_hd, 40 inference steps, guidance scale 4.0, safety checker on Supported parameters: prompt, model ID (any fal.ai model), number of images, image size, inference steps, guidance scale, safety checker. Images are saved locally; file paths are shown in the response. For model IDs, see fal.ai/models. ## Troubleshooting - FAL_KEY environment variable is not set: Set your fal.ai API key as above. - npx not found: Install Node.js 18+ and npm. Advanced: Example MCP Request/Response Request: { "tool": "generate-image", "args": { "prompt": "A futuristic cityscape at sunset", "model": "fal-ai/kolors" } } Example response: { "images": [ { "url": "file:///path/to/generated_image1.png" }, { "url": "file:///path/to/generated_image2.png" } ] } ## 📁 Image Output Directory Generated images are saved to your local system: - By default: ~/Downloads/fal_ai (on Linux/macOS; uses XDG standard if available) - Custom location: Set the environment variable FAL_IMAGES_OUTPUT_DIR to your desired folder. Images will be saved in /fal_ai. The full file path for each image is included in the tool's response. ## ⚠️ Error Handling & Troubleshooting - If you specify a model ID that is not supported by fal.ai, you will receive an error from the backend. Double-check for typos or visit fal.ai/models to confirm the model ID. - For the latest list of models and their capabilities, refer to the fal.ai model catalog or API docs. - For other errors, consult your MCP client logs or open an issue on GitHub. ## 🤝 Contributing Contributions and suggestions are welcome! Please open issues or pull requests on GitHub. ## 🔒 Security - Your API key is only used locally to authenticate with fal.ai. - No user data is stored or transmitted except as required by fal.ai API. ## 🛡 License MIT License © 2025 Madhusudan Kulkarni MCP fal.ai Image Server Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf. When and Why to Use This tool is designed for: - Developers and designers who want to generate images from text prompts without leaving their IDE. - Rapid prototyping of UI concepts, marketing assets, or creative ideas. - Content creators

Full README

MCP fal.ai Image Server

Effortlessly generate images from text prompts using fal.ai and the Model Context Protocol (MCP). Integrates directly with AI IDEs like Cursor and Windsurf.

When and Why to Use

This tool is designed for:

  • Developers and designers who want to generate images from text prompts without leaving their IDE.
  • Rapid prototyping of UI concepts, marketing assets, or creative ideas.
  • Content creators needing unique visuals for blogs, presentations, or social media.
  • AI researchers and tinkerers experimenting with the latest fal.ai models.
  • Automating workflows that require programmatic image generation via MCP.

Key features:

  • Supports any valid fal.ai model and all major image parameters.
  • Works out of the box with Node.js and a fal.ai API key.
  • Saves images locally with accessible file paths.
  • Simple configuration and robust error handling.

Quick Start

  1. Requirements: Node.js 18+, fal.ai API key
  2. Configure MCP: { "mcpServers": { "fal-ai-image": { "command": "npx", "args": ["-y", "mcp-fal-ai-image"], "env": { "FAL_KEY": "YOUR-FAL-AI-API-KEY" } } } }
  3. Run: Use the generate-image tool from your IDE.

💡 Typical Workflow: Describe the image you want (e.g., “generate a landscape with flying cars using model fal-ai/kolors, 2 images, landscape_16_9”) and get instant results in your IDE.

🗨️ Example Prompts

  • generate an image of a red apple
  • generate an image of a red apple using model fal-ai/kolors
  • generate 3 images of a glowing red apple in a futuristic city using model fal-ai/recraft-v3, square_hd, 40 inference steps, guidance scale 4.0, safety checker on

Supported parameters: prompt, model ID (any fal.ai model), number of images, image size, inference steps, guidance scale, safety checker.

Images are saved locally; file paths are shown in the response. For model IDs, see fal.ai/models.

Troubleshooting

  • FAL_KEY environment variable is not set: Set your fal.ai API key as above.
  • npx not found: Install Node.js 18+ and npm.

Advanced: Example MCP Request/Response

Request: { "tool": "generate-image", "args": { "prompt": "A futuristic cityscape at sunset", "model": "fal-ai/kolors" } }

Example response: { "images": [ { "url": "file:///path/to/generated_image1.png" }, { "url": "file:///path/to/generated_image2.png" } ] }

📁 Image Output Directory

Generated images are saved to your local system:

  • By default: ~/Downloads/fal_ai (on Linux/macOS; uses XDG standard if available)
  • Custom location: Set the environment variable FAL_IMAGES_OUTPUT_DIR to your desired folder. Images will be saved in /fal_ai.

The full file path for each image is included in the tool's response.

⚠️ Error Handling & Troubleshooting

  • If you specify a model ID that is not supported by fal.ai, you will receive an error from the backend. Double-check for typos or visit fal.ai/models to confirm the model ID.
  • For the latest list of models and their capabilities, refer to the fal.ai model catalog or API docs.
  • For other errors, consult your MCP client logs or open an issue on GitHub.

🤝 Contributing

Contributions and suggestions are welcome! Please open issues or pull requests on GitHub.

🔒 Security

  • Your API key is only used locally to authenticate with fal.ai.
  • No user data is stored or transmitted except as required by fal.ai API.

🛡 License

MIT License © 2025 Madhusudan Kulkarni

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingSmithery

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

MCP: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/contract"
curl -s "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "MCP"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "SMITHERY",
      "generatedAt": "2026-10-10T00:11:27.283Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "MCP",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:MCP|unknown|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Madhusudan Kulkarni",
    "category": "vendor",
    "href": "https://github.com/madhusudan-kulkarni/mcp-fal-ai-image",
    "sourceUrl": "https://github.com/madhusudan-kulkarni/mcp-fal-ai-image",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:48:51.823Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "MCP",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:48:51.823Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/smithery-madhusudan-kulkarni-mcp-fal-ai-image/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  }
]

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