omniref
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@omniref find image references for a moody neon-lit cyberpunk street"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
omniref
One MCP interface to search image and video references across many search engines, built for AI image and video generation agents (reference-to-video, style, character and motion conditioning).
Status: pre-alpha. Only a deterministic mock provider exists today. The tool schema is the part to review.
Run
Two transports, one binary. stdio is the default.
Transport | Use when | Command |
| The client spawns the server (Claude Desktop, Claude Code, Cursor) |
|
| Shared or remote server (streamable HTTP at |
|
The HTTP server has no authentication. It binds to localhost by default; put it behind an authenticating proxy before exposing it. The Docker image binds 0.0.0.0 inside the container, so publish the port to 127.0.0.1 only (-p 127.0.0.1:8000:8000).
From source
Requires Python 3.12+ and uv.
uv sync
uv run omniref # stdio
uv run omniref --transport http # http://127.0.0.1:8000/mcpFrom a GitHub release
Each tag vX.Y.Z publishes a wheel and sdist on the Releases page and a container image on GHCR.
uvx --from https://github.com/kaustubhkagrawal/omniref/releases/download/v0.1.0/omniref-0.1.0-py3-none-any.whl omniref
docker run -i --rm ghcr.io/kaustubhkagrawal/omniref:0.1.0 --transport stdio
docker run --rm -p 127.0.0.1:8000:8000 ghcr.io/kaustubhkagrawal/omniref:0.1.0Connect a client
# stdio
claude mcp add omniref -- uvx --from git+https://github.com/kaustubhkagrawal/omniref omniref
# http
claude mcp add --transport http omniref http://127.0.0.1:8000/mcpGeneric stdio config (Claude Desktop, Cursor):
{
"mcpServers": {
"omniref": {
"command": "uvx",
"args": ["--from", "git+https://github.com/kaustubhkagrawal/omniref", "omniref"]
}
}
}Related MCP server: multi-api-web-search
Releasing
Bump version in pyproject.toml, merge to main, then git tag vX.Y.Z && git push origin vX.Y.Z. The release workflow checks the tag matches pyproject.toml, runs lint and tests, builds the artifacts, creates the GitHub Release, and pushes ghcr.io/kaustubhkagrawal/omniref. Tags with a hyphen (e.g. v0.2.0-rc1) are marked prerelease and do not move latest.
Tools
Tool | Purpose |
| Configured providers and the media types each supports |
| Fan out one query to all (or selected) providers; returns normalized, interleaved, URL-deduplicated results plus per-provider |
Layout
src/omniref/
models.py Reference, SearchRequest, SearchResponse (the unified schema)
registry.py Parallel fan-out, timeouts, error isolation, merge/dedupe
server.py FastMCP server and CLI (stdio + http)
__main__.py python -m omniref
providers/
base.py ReferenceProvider interface
mock.py Fake data for dev and tests
skills/
omniref/SKILL.md Agent skill that teaches the workflow
tests/Adding a provider
Subclass ReferenceProvider, set name, description, media_types, implement async search(request) -> list[Reference], and register it in server.default_registry(). Map the native payload into Reference; raise on failure and the registry reports it in errors without affecting other providers.
Develop
uv run pytest
uv run ruff check . && uv run ruff format --check .License
MIT. See LICENSE.
Available Tools
2 toolslist_providersList ProvidersARead-only
List configured reference search providers and the media types each supports.
Inputs: no named parameters. Returns: result
search_referencesSearch ReferencesARead-only
Search images and videos across all providers; results are normalized and deduplicated.
Partial failures are reported in errors while other providers' results are still returned.
Inputs: query (required), limit, providers, media_types. Returns: query, errors, results, providers_queried
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools serve clearly distinct purposes: one enumerates available providers and their supported media types, the other performs the actual cross-provider search. An agent cannot reasonably confuse discovery of providers with executing a reference search.
Both names follow the same snake_case verb_noun convention (list_providers, search_references) and use consistent terminology aligned with the domain (providers, references).
Two tools is thin for a server that otherwise implies a multi-provider search surface; discovery and search alone leave little room for the operations users would expect. The count is defensible for a minimal read-only server but borderline under-scoped.
The surface covers the two core read operations (enumerate providers, search across them) but omits obvious adjacent operations such as retrieving provider details/capabilities, fetching a single reference by ID, or scoping a search to one provider. Agents can work around these gaps via search_references but may hit dead ends for provider-specific inspection.
Maintenance
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Multi-engine search for AI agents. Trust scoring, local corpus, MCP-native. Self-hostable, BYOK.
Search over 1.85 million captioned images using text, a reference image, or both. Upload a JPEG, PNG, or WebP image as base64, or provide a public HTTPS image URL. Find similar images from a Lightdrift asset ID and retrieve hosted file URLs, source licenses, and attribution. Connect with OAuth or an API key. Searches cost $0.005 ($5 per 1,000); image details are free. Setup: https://docs.lightdrift.ai/guides/images-mcp
Live AI-native web search with citations. One tool for every MCP client. Flat per-request pricing.
Free web search for AI agents. No API key required. Hosted MCP in active development.
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