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mimo-vision · Native vision plugin for DSH

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mimo-vision is a native plugin for DeepSeek Harness (DSH), package name mimo-vision. It registers a describe_image tool that sends an image to a mimo-v2.5-series multimodal model and returns the text description to the main model — a "vision bridge" built for main models (e.g. deepseek-v4-flash) that have no vision input of their own.

It is not a standalone process: it is a first-class citizen of DSH's "everything is a plugin" model. apply does exactly one thing — registers the capability as a first-class dsh tool. Dependencies, files, credentials, and subprocesses all go through dsh's defined capability seams; uninstalling cleans up cleanly.

Implementation paradigm: a direct landing of DSH's native plugin primitives

  • Registration is reversible by construction: apply(ctx) contains a single ctx.tools.register(defineTool(...)). register() returns a disposer; when the plugin fiber disposes, the tool is unregistered and its schema is automatically withdrawn from the system prompt. There is no leftover cleanup code — clean uninstall is a structural guarantee, not hand-written cleanup.

  • inject declares dependencies: export const inject = ['tools', 'fs', 'credentials'] follows the pure Cordis effect spec — activation only happens once the seams are in place. ctx.get('subprocess') in transcode.ts is an optional capability with a default fallback, used at execution time, not activation time. This is "declared dependencies", not "probed dependencies".

  • All capabilities go through dsh seams: file reads use ctx.fs.resolve/stat/readBytes + ctx.emit('fs/observed'); credentials use ctx.credentials.resolve(credentialRef(...)) with no hand-rolled parsing; subprocess (transcoding) uses ctx.subprocess. The one exception: transcode temp files are written via node:fs to the system temp directory (see Known Limitations below).

  • Uninstallable / composable: uninstall is pure — the disposer runs and everything is reclaimed: no disk writes, no timers, no long-lived connections to manually wind down.

Tool

Tool

Arguments

Description

describe_image

path (required), question (optional)

Describes an image file and returns text

Supported image formats:

  • Sent natively: PNG / JPEG / GIF / WebP / BMP (all verified decodable by the vision model)

  • Auto-transcoded: SVG / TIFF (.tif) / HEIC (.heif) / PSD / ICO / EXR / JP2 / JXL / AVIF — when ImageMagick is installed locally, these are transcoded to PNG before sending, downscaled to a ≤2048px long edge (saves tokens)

  • Any other extension, or a transcode-format when ImageMagick is not installed, returns an explicit error (never sent silently)

Usage example (tell the model): Use describe_image to describe D:\photos\cat.png, focusing on what breed of cat it is.

Related MCP server: vision-mcp

How it works

  1. Key resolution: ctx.credentials.resolve takes the first non-empty of OPENCODE_GO_API_KEYOPENCODE_API_KEY (DSH credential layering: process env > ~/.dsh/.credentials.yaml > .env).

  2. Read the image: ctx.fs.resolve (relative paths resolve against the session workspace cwd) → ctx.fs.readBytes (20 MiB cap); non-native formats (SVG/TIFF/HEIC…) are transcoded to PNG via ImageMagick (through the ctx.subprocess seam) and downscaled to a 2048px long edge → base64.

  3. Routing: the free Zen route (mimo-v2.5-free) is tried first; on failure (non-2xx / timeout) it falls back to the paid Go route (mimo-v2.5) once per request; allowPaid: false disables the paid fallback.

Design decisions are documented in adr/0002-dsh-native-plugin.md (which supersedes the earlier MCP approach, ADR-0001).


For npx @deepseek-ai/dsh web or a globally installed DSH. The repo ships prebuilt artifacts (lib/index.js) — no build toolchain required.

Prerequisite: Node ^22.19 || >=24, and DSH already able to start.

Step 0 · Install and activate (one command)

mimo-vision declares dsh.bundle (see the dsh field in package.json), so dsh plugin add reconciles it as a bundle layer of the profile — installing the package, mounting the layer, and activating the tool happen in one step:

# from the npm registry (recommended once this package is published)
dsh plugin --profile web add mimo-vision

# from GitHub (source install, prebuilt lib/)
dsh plugin --profile web add github:wulusai2333/mimo-vision

If the repo is not on its default branch, use github:wulusai2333/mimo-vision#<branch-or-tag>. This path relies on the dependency closure that DSH maintains in ~/.dsh/profiles/node_modules (symlinking all @deepseek-ai/* seams to the dsh install tree), so the plugin's runtime import "@deepseek-ai/dsh-tools" etc. resolves to the same instance DSH uses — singleton-safe, register()/inject semantics unchanged. GitHub-source installs require this repo to commit a prebuilt lib/ (kept in sync with src/): pnpm installs the source with lib/ included, no build runs, no allowBuilds needed.

Manually copy the artifacts into the profile's plugin resolution root, then mount the patch in the profile's cordis.patch.yml (equivalent to automatic bundle mounting, but two manual steps):

# Windows PowerShell
$dst = "$env:USERPROFILE\.dsh\profiles\node_modules\mimo-vision"
New-Item -ItemType Directory -Path $dst -Force | Out-Null
Copy-Item package.json -Destination $dst -Force
Copy-Item cordis.patch.yml -Destination $dst -Force
Copy-Item lib -Destination $dst -Recurse -Force
# macOS / Linux
dst="$HOME/.dsh/profiles/node_modules/mimo-vision"
mkdir -p "$dst"
cp package.json "$dst/"
cp cordis.patch.yml "$dst/"
cp -r lib "$dst/"

Then edit ~/.dsh/profiles/web/cordis.patch.yml and add:

- insert:
    - id: tool-vision
      name: 'mimo-vision'
      config:
        allowPaid: true

Step 1 · Configure the key

Put an opencode key in ~/.dsh/.credentials.yaml (OPENCODE_GO_API_KEY preferred, OPENCODE_API_KEY as fallback):

OPENCODE_GO_API_KEY: sk-...

You can also put it in the environment of the process that starts DSH (OPENCODE_GO_API_KEY=... dsh web). Credential seam layering priority: process env > .credentials.yaml > .env.

Step 2 · Restart and verify

A restart is required on first integration (so the process imports the new package). After that, changes to this plugin's code or config such as allowPaid hot-reload without restart.

After restarting, verify: mimo-vision should appear in DSH's settings, with describe_image among the available tools; or just tell the model "use describe_image to describe some image" and try it.

Uninstall

dsh plugin remove takes the package name (the key in the profile's dependencies), not the install source:

dsh plugin --profile web remove mimo-vision

Passing github:wulusai2333/mimo-vision errors with ERR_PNPM_CANNOT_REMOVE_MISSING_DEPS ("no such dependency found") — the dependency is recorded by package name mimo-vision, so remove needs that key. The command removes both the dependency and the bundle layer.


Configuration (all optional, defaults provided)

Field

Default

Description

allowPaid

true

Whether to fall back to the paid route after the free route fails

freeBaseUrl

https://opencode.ai/zen/v1

Free route base URL

freeModel

mimo-v2.5-free

Free route model

paidBaseUrl

https://opencode.ai/zen/go/v1

Paid route base URL

paidModel

mimo-v2.5

Paid route model

A minimal registration only sets allowPaid; everything else uses the defaults:

- insert:
    - id: tool-vision
      name: 'mimo-vision'
      config:
        allowPaid: false

Building from source / development (DSH monorepo)

To modify the plugin source, put it into the DSH source tree and go through the repo gates:

# 1. Copy this repo as packages/vision/tool-vision
cp -r /path/to/mimo-vision <dsh>/deepseek-harness/packages/vision/tool-vision

# 2. Install and verify (tsc typecheck + vitest unit tests + oxlint)
cd <dsh>/deepseek-harness
pnpm install
npx tsc -b packages/vision/tool-vision   # typecheck
npx vitest run packages/vision/tool-vision   # unit tests
npx oxlint packages/vision/tool-vision       # lint

# 3. Produce lib/index.js (prebuilt artifacts)
cd packages/vision/tool-vision
pnpm run build

pnpm run build runs tsc --build first (emitting the intermediate lib/types/*.js that tsdown consumes) and then bundles lib/index.js / lib/invariant.js. Steps 2/3 can also be run inside the package with pnpm run test / pnpm run typecheck / pnpm run build (the scripts are in package.json).

Note: the @deepseek-ai/dsh-* seams are versioned by the DSH dependency closure, not by this package. The peer dependencies are marked optional so a standalone npm install mimo-vision succeeds, but the plugin only runs inside DSH (where the closure provides those seams as a singleton). For development, either use the prebuilt artifacts from "Quick install" above, or run it from source inside the monorepo.

Failure semantics

Any failure (no key, unsupported format, both routes failing, file missing / not a regular file, over the limit, non-image response) is returned as a tool-level error: execute throws, the registry materializes isError, the process does not exit and the session does not break.

Known Limitations

  • Transcode temp files bypass the ctx.fs sandbox: when non-native formats (SVG/TIFF/HEIC…) are transcoded via ImageMagick, the source bytes and the resulting PNG go through node:fs to the system temp directory (os.tmpdir()), because ctx.subprocess needs real OS paths to feed the local magick, while ctx.fs's FsTarget may be an abstract/remote/sandboxed path. This bypasses the dsh-fs-sandbox file sandbox policy; the temp directory is deleted immediately after conversion. It only triggers when ImageMagick is installed locally and a transcode format is requested — native formats (PNG/JPEG/GIF/WebP/BMP) never take this path.

  • The prebuilt lib/ must stay in sync with src/: GitHub-source installs load the committed lib/index.js directly and never build at install time. After changing src/, rebuild and commit lib/, or the deployed version loads stale artifacts.

Security

  • The key is only read via ctx.credentials.resolve — never printed, never written to disk, never directory-scanned;

  • The key is only used in the request header Authorization: Bearer ...;

  • Images are read into memory via ctx.fs and sent straight as base64, never written to disk (non-native formats are routed through the system temp dir during transcoding and deleted immediately after).

License

MIT

Available Tools

1 tool
describe_imageA

Describe an image file using a vision model and return a textual description.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the image file.
questionNoOptional question about the image.

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, leaving the description to carry the full burden of behavioral disclosure. It discloses that it returns a textual description, but it fails to mention the optional question handling, any operational details, limitations, or potential side effects. For a tool that likely only reads an image, more context is expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, concise sentence that front-loads the core action. Every word earns its place, and the structure is clear and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and no output schema, the description provides the essential return behavior ('return a textual description'). It could be more complete by mentioning the optional question capability, but the schema already covers that parameter. Overall, it is adequately complete for its simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters described. The description does not add any parameter-specific meaning beyond the schema; it simply re-states the action. The optional question parameter is not mentioned in the description, but the schema already covers it, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action—describing an image file using a vision model—and its output is a textual description. It is unambiguous and, despite having no siblings, effectively communicates its unique function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There are no sibling tools or explicit alternatives, so it cannot provide when-not guidance. However, the description clearly implies its use case via 'using a vision model' and 'image file', providing clear context. It does not explicitly exclude other uses, but that is acceptable given no alternatives exist.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

A3.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusing it with another tool. The tool's purpose is clearly defined.

Naming Consistency5/5

The single tool uses a clear verb_noun pattern ('describe_image'), which is consistent with common naming conventions. There are no other tool names to conflict.

Tool Count2/5

Having only one tool for a vision-related server feels too few for the apparent scope. A vision server typically requires more capabilities such as image classification, object detection, or generation, making this single tool insufficient.

Completeness1/5

The tool surface is severely incomplete for a vision server. Relying solely on image description leaves many common vision tasks unaddressed, creating significant gaps that would cause agent failures.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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