digitalassetscan-mcp
Official# Digital Asset Scan MCP
<!-- mcp-name: org.digitalassetscan/digitalassetscan-mcp -->
`digitalassetscan-mcp` is a lightweight stdio Model Context Protocol adapter
for the public Digital Asset Scan API. Digital Asset Scan is the product; CAI
is its analytical engine and methodology.
The package exposes four tools: `analyze_asset`, `get_analysis_job`,
`list_assets`, and `get_methodology`. It contains no analytical or blockchain
RPC implementation, stores no responses, needs no credentials, and has no
third-party runtime dependencies. It requires outbound HTTPS access to
`https://api.digitalassetscan.org`.
Analysis admission is asynchronous. Use `get_analysis_job` to poll an admitted
job to a terminal operational state. Analytical `UNKNOWN` and `UNRESOLVED`
states are valid results, not MCP transport failures. Score is currently
withheld because no validated scoring construct is published; Confidence is
reported independently when valid.
This source tree can be built and installed in an isolated environment. Public
PyPI publication has not occurred:
```console
python -m build
python -m venv /tmp/digitalassetscan-mcp-venv
/tmp/digitalassetscan-mcp-venv/bin/python -m pip install dist/digitalassetscan_mcp-0.1.0-py3-none-any.whl
```
Generic stdio client configuration after installation:
```json
{
"mcpServers": {
"digitalassetscan": {
"command": "digitalassetscan-mcp",
"args": []
}
}
}
```
The process reads one JSON-RPC message per line from stdin and writes only MCP
JSON-RPC framing to stdout. Closing stdin shuts it down. Analysis admission is
asynchronous: poll the returned job with `get_analysis_job` until terminal.
Results are neutral analytical evidence for informational use, not investment
advice or a prediction of asset quality, safety, merit, price, or future
performance.
Digital Asset Scan MCP is released under the MIT License. Copyright (c) 2026
CurrenC Corporation.
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: submitting an analysis, polling for results, listing assets, and retrieving methodology. There is no overlap or ambiguity in what an agent would use each tool for.
All tool names follow a consistent verb_noun snake_case pattern: analyze_asset, get_analysis_job, list_assets, get_methodology. The naming is predictable and easy to navigate.
Four tools is well-scoped for a focused asset-scanning server. Each tool serves a necessary part of the workflow: discover assets, understand methodology, submit analysis, and retrieve results.
The core async analysis lifecycle is covered with submit and poll, and the supporting discovery/methodology tools are present. Minor gaps exist, such as no explicit cancel operation or asset detail retrieval, but the primary workflow is fully usable.