Binary Ninja MCP vs Retro Diffusion Pixel Art
A side-by-side look at the Binary Ninja MCP and Retro Diffusion Pixel Art MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Binary Ninja MCP for security work as a local process; choose Retro Diffusion Pixel Art for security over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Binary Ninja MCP when
- You need dedicated capabilities in the Security domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
list_methods, get_entry_points, search_functions_by_name.
Choose Retro Diffusion Pixel Art when
- You need dedicated capabilities in the Security domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
authenticate, logout, create_user_style.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Security | Security |
| Quality signal | 71/100 · Great | 57/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 455 | 7 |
| Directory activity | 5 views 1 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | 54 | 33 |
| Full listing | View Binary Ninja MCP | View Retro Diffusion Pixel Art |
Tools & capabilities
Binary Ninja MCP tools 54
list_methodsList all function names in the program with pagination.get_entry_pointsList entry point(s) of the loaded binary.search_functions_by_nameSearch for functions whose name contains the given substring.decompile_functionDecompile a specific function by name and return the decompiled C code.get_ilGet IL for a function in the selected view (hlil, mlil, llil).fetch_disassemblyRetrieve the disassembled code of a function as assembly mnemonic instructions.rename_functionRename a function by its current name. The configured prefix will be automatically prepended if not present.rename_single_variableRename a variable in a function.rename_multi_variablesRename multiple local variables in one call.rename_dataRename a data label at the specified address.set_commentSet a comment at a specific address.get_commentGet the comment at a specific address.- +42 more on the listing page
Retro Diffusion Pixel Art tools 33
authenticateAuthenticate this MCP session with a RetroDiffusion public API key.logoutClear the authenticated RetroDiffusion API key for this MCP session.create_user_styleCreate a user style from the public RD Pro template using /v2/styles. Use `style_reference_images` and `style_reference_caption` for style-level references. These are baked into the custom style and are not the same as per-inference `reference_images`.update_user_styleUpdate a public RD Pro user style using /v2/styles/{style_id}. Use `style_reference_images` and `style_reference_caption` for style-level references. These are baked into the custom style and are not the same as per-inference `reference_images`.delete_user_styleDelete a user style using /v2/styles/{style_id}. style_id can be either internal id or prompt_style/public_id.list_available_stylesList publicly available styles from /v2/styles/selector. Use this to discover valid prompt_style values plus style metadata such as require_input_image and supports_reference_images.list_available_modelsReturn unique public model identifiers available in /v2/styles/selector.get_style_usageExplain how to use a public prompt_style from the RetroDiffusion API. Use this before create_inference if you are unsure whether a style expects `input_image`, supports per-inference `reference_images`, or whether you meant style-level `style_reference_images`.get_balanceGet credits/balance using the public /v2/inferences/credits endpoint.estimate_inference_costEstimate generation cost using the public /v2/inferences endpoint with check_cost=true. Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style.create_inferenceGenerate images using the public /v2/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__*); they support reference_images for character/style consistency, and most go as small as 12x12 px (check list_available_styles for each style's limits) — a small target size is never a reason to switch to a cheaper model family. Style ids are opaque strings with no uniform format (some RD Fast styles appear as "default:rd_flux"); take them verbatim from the catalog and never infer capabilities from an id's prefix. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running, and a failed animation is worth one retry with identical parameters (failures auto-refund). Field-tested workflow rules: N distinct items = N individually usable images (separate calls or num_images=N), never one sheet/grid image unless a sheet IS the deliverable. Variants of ONE image (seasons, day/night, palettes) = generate the base once, then derive each variant with the image_edit tool ("... keep the exact same composition") — independent generations of the "same" scene come out unrelated. Converting an existing image INTO pixel art is rd_pro__pixelate with input_image (16-256 px output, batch<=16); reference_images-based generation re-imagines rather than converts. To animate an image you already have, use rd_advanced_animation__* with input_image (fixed-format rd_animation__* styles generate their own subject from the prompt instead). To get the other directional views of a sprite you already have, use rd_advanced_animation__rotate with input_image (same 8-direction layout as rd_animation__8_dir_rotation). Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.list_edit_toolsList the enabled canvas edit tools from /v2/edit/tools with their fields, costs, and limits. Edit tools are the recommended way to post-process generated pixel art: background removal, palette conversion, color reduction, pixel correction, rotation, inpainting, outpainting, seam tiling, and prompt-driven edits.- +21 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Binary Ninja MCP
{
"mcpServers": {
"fosdickio-binary-ninja-mcp": {
"command": "npx",
"args": [
"-y",
"binary-ninja-mcp"
]
}
}
}
Retro Diffusion Pixel Art
{
"mcpServers": {
"retro-diffusion-pixel-art": {
"url": "https://mcp.retrodiffusion.ai/mcp"
}
}
}
Frequently asked questions
Binary Ninja MCP is categorized under Security and uses a local stdio subprocess. Retro Diffusion Pixel Art belongs to Security and uses a remote HTTP/SSE transport. Pick Binary Ninja MCP for security work and Retro Diffusion Pixel Art for security.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Binary Ninja MCP is listed as free. Auth: no auth required. Retro Diffusion Pixel Art has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add fosdickio-binary-ninja-mcp and retro-diffusion-pixel-art as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Binary Ninja MCP has a quality signal of 71/100 (Great) with 5 views, 1 installs and 455 GitHub stars. Retro Diffusion Pixel Art has 57/100 (Good) with 0 views, 0 installs and 7 GitHub stars.