Tancky AI is a free online AI image combiner that merges two or more pictures into one natural, photorealistic scene. Upload your photos, describe the composition, and the AI image combiner matches lighting, perspective and shadows for you. No Photoshop, no manual masking.
👉 Try the AI image combiner free: https://tancky.io
An AI image combiner is a photo merger that brings multiple pictures into one cohesive composition. A traditional collage places photos side by side in bordered frames, leaving visible seams and mismatched lighting. An AI image combiner analyzes the subjects, depth and ambient light across every reference image, then renders a single new photograph in which every element looks like it was captured in the same shot.
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Bring distant family members, friends or partners together in a single portrait. The AI image combiner preserves recognizable facial features, hair textures and expressions while harmonizing scale, eye levels and skin tone illumination.
Pair an everyday headshot with a travel destination or studio setting. Depth of field, horizon lines and sunlight angles are matched so the subject sits naturally inside the scene.
Place a product cutout into a styled lifestyle scene. Packaging shapes, brand typography and logos stay sharp, with contact shadows and reflections added automatically.
Insert a furniture reference into a photo of your room. The room's flooring, walls and decor are preserved, and the new piece is rendered at a realistic scale.
Step 1: Upload | Step 2: Describe | Step 3: Generate |
Upload 2 to 4 pictures in JPG, PNG or WebP format. Clear, well lit photos work best. | Describe how the subjects should be positioned, lit and framed. | Pick an aspect ratio, click Generate, then review and download the result. |
Photorealistic light and perspective fusion. Contrasting light sources, color temperatures and camera angles are harmonized, with cast shadows and reflections synthesized automatically.
Facial fidelity and product detail preservation. Faces, hair textures and brand labels are retained with high fidelity.
Zero manual masking. No pen tools, clipping paths or feathering. Subjects are extracted and composited for you.
High-resolution output with commercial rights. Use the results in ads, e-commerce listings, client pitches and social media.
Free to start. New users receive trial credits on signup. Credit packs never expire.
Start with sharp, high-resolution source images where the subject is in clear focus.
Pair photos with compatible camera angles, such as an eye-level portrait with an eye-level background.
State in the prompt which image supplies the background and where each subject should be placed.
Mention details that must stay unchanged, such as a logo, an outfit or a piece of furniture.
How is an AI image combiner different from a photo collage? A collage arranges pictures side by side in a grid. An AI image combiner blends the subjects into one scene with unified depth, perspective and lighting.
Can I combine multiple images into one photo? Yes. You can combine up to 4 source pictures at once.
Does the AI image combiner preserve faces and logos? Yes. Facial features, expressions and hair are preserved in portraits, and packaging shapes, typography and logos stay clear for products.
Can I use the AI image combiner for free? Yes. Every new user gets trial credits on signup, with optional credit packs and subscriptions afterwards.
Can I use the generated images commercially? Yes. Finished composites include commercial rights.
Tancky AI is listed in the official MCP Registry as io.github.tancky777/ai-image-combiner. Connect any MCP client (Claude, Cursor, VS Code and others) to the remote endpoint below to let your assistant find the right AI image combiner tool, its link and step-by-step guides. Source code: mcp/.
Endpoint: https://tancky-mcp.tancky777-482.workers.dev/mcp
Transport: Streamable HTTP
Authentication: none, read only
{
"mcpServers": {
"tancky-ai": {
"type": "http",
"url": "https://tancky-mcp.tancky777-482.workers.dev/mcp"
}
}
}Tool | What it does |
| Lists the Tancky AI photo tools with links and descriptions. Optional |
| Finds Tancky AI guides on combining and blending photos. Optional |
To run it locally over stdio instead (Node.js 22.18 or later):
cd mcp && npm ci --omit=dev
node src/stdio.tsAI Image Combiner: https://tancky.io
Prompt guide: AI Image Combiner Prompt Guide
Overview: AI Image Combiner on Google Sites
Pricing: https://tancky.io/pricing
Updates: https://tancky.io/updates
List the Tancky AI photo tools (AI image combiner, couple, family and wedding photos, background remover, upscaler) with links and descriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Language of the returned links, defaults to en |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered structurally. The description adds only that results come 'with links and descriptions,' which is useful return context but not deep behavioral disclosure; any pagination or rate-limit behavior is unstated.
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?
One sentence, front-loaded with the verb and resource, and the parenthetical enumeration of tools is information-bearing rather than filler. Slightly dense but nothing wasted.
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?
For a simple, safe read-only listing tool with one optional parameter, no output schema, and full annotation coverage, the description tells the agent what it returns and which language it can request. Only sibling differentiation is missing.
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?
There is a single optional locale parameter with 100% schema description coverage (enum en/zh, default en), so the schema fully documents it. The description adds nothing about the parameter, so the baseline 3 applies.
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 states a specific verb (List) and resource (Tancky AI photo tools) and even enumerates the catalog contents, so an agent knows exactly what it returns. It does not, however, differentiate itself from the sibling search_guides, leaving that distinction to inference.
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?
Usage is only implied: a catalog-listing tool for discovering available photo tools. There is no explicit statement of when to prefer this over search_guides, no prerequisites, and no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find Tancky AI guides about combining, blending and editing photos with AI. Omit query to list all guides.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keywords, for example "blend two photos" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld=false, so the safety profile is fully covered. The description adds the no-query listing behavior, but says nothing about result limits, ranking, or matching semantics.
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?
Two short sentences with zero filler; the core purpose is front-loaded and the optional-parameter behavior follows immediately.
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?
For a single-optional-param search tool with annotations carrying the safety profile, the description is nearly sufficient. With no output schema, it could have said what a result looks like (e.g., guide titles/snippets), but that is a minor gap.
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 coverage is 100% and the parameter is optional with a documented example ('blend two photos'). The description adds the omit-query behavior, but that is already implied by the schema having no required fields, so little net-new meaning is contributed.
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?
States a specific verb ('Find') and resource ('Tancky AI guides') with domain scoping (combining, blending, editing photos). It doesn't distinguish itself from the sibling list_tools, whose name overlaps with the 'list all guides' behavior described here.
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?
'Omit query to list all guides' gives explicit guidance for one usage branch, which is genuinely useful. However, it never contrasts with the sibling list_tools, so an agent cannot tell when to prefer this tool over that one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0list_toolssearch_guidesScored across 2 tools
The two tools target different resource types (product photo tools vs. editorial guides), so their purposes are largely distinct. However, 'list_tools' is easily confused with the MCP meta-convention of listing the server's own tools, which creates a real risk of misselection despite the description clarifying it.
Both names follow a clean verb_noun snake_case pattern (list_tools, search_guides). No mixing of conventions or vague verbs.
Two tools is very thin for a server whose name implies an image-combining capability. There is no tool that actually performs or configures the core function, so the surface feels under-built rather than well-scoped.
The server is named 'tancky-ai-image-combiner' yet offers no tool to combine, blend, or process images — only catalog lookup and guide search. Core lifecycle operations for the stated domain are missing, leaving agents with dead ends for the primary task.
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