Which integrations are available for this server?

Integrates with OpenAI-compatible APIs for LLM-based extraction of reaction steps and generating embeddings for semantic search, using configurable endpoints for chat completions and embeddings. Allows use of PostgreSQL as the primary database backend with optional pgvector support for vector storage and similarity search, including connectivity status and fallback to SQLite. Provides optional Redis as a queue backend for durable job processing, configurable via queue.redis_url as an alternative to the default SQLite queue.

How do I use scifinder-route-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@scifinder-route-mcp scan inbox for new SciFinder exports" 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.

scifinder-route-mcp

by kettly1260

Overview Schema Related Servers Score Discussions

Python

Local

scifinder-route-mcp

NAS-hosted MCP server for indexing and searching reaction-step-level synthesis routes from local SciFinder exports. It is designed to run long-term on Docker/NAS with a read-only inbox, durable SQLite queue fallback, optional external OCR/LLM/vector/parser/structure-recognition APIs, and an operational Admin Web UI.

GHCR visibility note: if anonymous pull fails, open GitHub → Packages → scifinder-route-mcp → Package settings → Change visibility → Public. The compose file is already configured for ghcr.io/kettly1260/scifinder-route-mcp:latest.

Quick Start With Prebuilt Image

The published Docker image targets both linux/amd64 and linux/arm64.

git clone https://github.com/kettly1260/scifinder-route-mcp.git
cd scifinder-route-mcp
cp .env.example .env
mkdir -p nas-data nas-inbox
docker compose -f docker-compose.image.yml up -d

Then open:

Admin Web UI: http://<nas-host>:8001/
MCP SSE:      http://<nas-host>:8000/sse

Put SciFinder exports into nas-inbox, then click Scan Inbox in the Admin Web UI or call the MCP scan_inbox tool. The image compose file uses image: only and does not build locally.

Local Build Deployment

docker compose up -d --build

Persistent paths:

./nas-data  -> /data
./nas-inbox -> /inbox (read-only in the container)
./nas-data/uploads -> /data/uploads (HTTP upload and sidecar staging)

Parsing is asynchronous in the NAS profile. Jobs are stored durably in SQLite; after a container restart, interrupted running jobs are re-queued. Poll get_parse_job_status or list_parse_jobs until completion.

Environment and Runtime Config

Copy .env.example to .env. Docker-level settings such as published ports, volumes, container network, and restart policy belong in .env/Compose only. The Admin Web UI never edits Docker files and never controls host Docker.

Hot application config is read from /data/config.yaml; copy config.example.yaml to ./nas-data/config.yaml if desired. Hot-reloadable sections include:

server.async_jobs, server.max_workers, server.storage_backend
queue.backend, queue.redis_url
security.allow_external_paths, security.token, security.users
ingest.scan_extensions
integrations.*
extraction.llm_schema_version, extraction.llm_prompt_profile, extraction.llm_cost_limit_usd
thresholds.verification_confidence_threshold
retention.evidence_retention_days, retention.cache_retention_days

Use MCP tools get_config, update_config, validate_config, and reload_config, or use the Admin Web UI.

Admin Web UI

The Admin Web UI provides operational controls for:

- health/status cards and mounted storage diagnostics
- token-protected config changes
- queue status, recent jobs, failed-job retry
- HTTP upload endpoint for sidecar/client upload
- LLM endpoint/model/enable toggle, schema version, prompt profile, cost limit
- embedding endpoint/model, vector rebuild, vector index status and errors
- OCR endpoint/model, OCR backlog status
- document parser endpoint/model, parser fallback and endpoint health
- structure recognition endpoint/model health
- PostgreSQL URL/backend status with SQLite fallback
- DOI low-confidence queue count
- evaluation latest metrics
- SQLite backup, retention dry-run cleanup, NAS storage usage
- compound registry count and search via MCP

MCP Tools

Implemented tools:

health_check
get_config
update_config
validate_config
reload_config
scan_inbox
register_document
upload_document
get_parse_job_status
list_parse_jobs
retry_parse_job
retry_failed_jobs
search_reaction_steps
get_reaction_step
get_reaction_provenance
record_doi_verification
reparse_document
export_evaluation_set
compute_evaluation_metrics
get_evaluation_status
rebuild_vector_index
get_vector_index_status
semantic_search_reaction_steps
search_compounds
get_compound
merge_compounds
search_by_smiles
recognize_structure_image
backup_database
get_storage_usage
cleanup_evidence_cache
test_integration_endpoint

Feature Matrix

Area	Status	Notes
Docker/NAS SSE service	Implemented	Compose and prebuilt-image compose supported.
GHCR multi-arch image workflow	Implemented	`linux/amd64`, `linux/arm64`. GHCR package visibility may need manual public setting.
Read-only inbox scanning	Implemented	`/inbox` mounted read-only.
HTTP upload staging	Implemented	`POST /api/upload` writes to `/data/uploads`; hash dedupe supported.
Sidecar watcher	Implemented	`scifinder-route-sidecar` polling CLI uploads stable files.
Durable queue	Implemented	SQLite queue is default; restart recovery and retry tools. Redis is optional/degraded via config status, not required.
SQLite storage	Implemented	Source documents, jobs, reaction steps, provenance, DOI verification, vector rows, compounds, metrics.
PostgreSQL backend	Runnable degraded integration	`SCIFINDER_ROUTE_BACKEND=postgres` tests connectivity and reports status; SQLite remains active fallback unless a Postgres adapter is added for a deployment.
pgvector	Optional/degraded	SQLite stores embeddings as JSON and cosine-searches them; Postgres/pgvector reports endpoint/backend status.
PDF/HTML/MHTML/text parsing	Implemented	Built-in parser remains fallback.
External document parser	Implemented	`/parse` JSON adapter; failure falls back unless disabled.
OCR worker	Implemented adapter	`/ocr` JSON adapter for image-only PDFs/low-text docs; errors are job errors, not service crashes.
Rule extraction	Implemented	Candidate blocks and structured fields.
LLM JSON structuring	Implemented adapter	OpenAI-compatible `/chat/completions`; strict JSON; invalid responses fall back to rule fields with metadata error.
Embedding/vector index	Implemented adapter	OpenAI-compatible `/embeddings`; rebuild/status/semantic search tools.
Compound registry	Implemented	CAS/SMILES/InChIKey text extraction, alias registry, reaction roles; RDKit optional.
Image structure recognition	Implemented adapter	`/recognize` adapter creates low-confidence image candidates; does not overwrite text evidence.
Multi-user authorization	Implemented	`viewer`, `operator`, `admin` roles via `SCIFINDER_ROUTE_USERS` or config users. Legacy single token maps to admin.
Evaluation metrics	Implemented	JSONL gold-set metrics and latest metric status.
Backup/retention	Implemented	SQLite backup, storage usage, evidence/cache cleanup dry-run.
Endpoint health checks	Implemented	LLM, embedding, OCR, parser, structure recognition, Postgres.

External API Schemas

All external services are optional. If a service is not configured or fails, the server returns a degraded/skipped/error status instead of crashing the process.

Embedding endpoint: POST <endpoint>/embeddings

{"model":"bge-m3","input":["text"]}

Expected response can be OpenAI-like:

{"data":[{"embedding":[0.1,0.2]}]}

LLM endpoint: POST <endpoint>/chat/completions, OpenAI-compatible. The assistant content must be strict JSON with reaction-step fields.

OCR endpoint: POST <endpoint>/ocr

{"model":"mineru-layout","file_path":"/data/uploads/file.pdf"}

Expected response:

{"text":"OCR text", "confidence":0.85}

Document parser endpoint: POST <endpoint>/parse

{"model":"parser-name","file_path":"/data/uploads/file.pdf"}

Expected response:

{"file_type":"pdf","title":"...","doi":"10....","chunks":[{"text":"...","page_number":1,"parser_name":"external","parser_version":"1"}]}

Structure recognition endpoint: POST <endpoint>/recognize

{"model":"decimer","image_path":"/data/evidence/page1.png"}

Expected response:

{"structures":[{"smiles":"CCO","confidence":0.7}]}

Sidecar Watcher

Create sidecar.yaml on a client machine:

watch_dir: /path/to/scifinder/exports
server_url: http://nas-host:8001
token: change-me
include_patterns:
  - "*.pdf"
  - "*.html"
settle_seconds: 3
upload_mode: http
poll_seconds: 2

Run:

scifinder-route-sidecar sidecar.yaml

The sidecar polls by default and does not require watchdog, making it suitable for Windows/macOS/Linux clients.

Authorization

Legacy single-token mode:

SCIFINDER_ROUTE_TOKEN=change-me

Multi-user token mode:

SCIFINDER_ROUTE_USERS=alice:viewer-token:viewer,bob:operator-token:operator,root:admin-token:admin

Roles:

viewer   search/read/status
operator scan/reparse/retry/vector/evaluation/integration tests
admin    config/backup/cleanup/secret operations

Development

python -m pytest -q

Optional Docker check:

docker compose build
docker compose -f docker-compose.image.yml config

Install Server

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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