BICScan MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| BICSCAN_API_KEY | Yes | API key obtained from https://bicscan.io for accessing BICScan risk scoring and asset information. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_risk_scoreB | Get Risk Score for Crypto, Domain Name, ENS, CNS, KNS or even Hostname Address |
| get_assetsB | Get Assets holdings by CryptoAddress |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: get_assets retrieves asset holdings, while get_risk_score calculates risk scores. There is no overlap in functionality, and an agent can easily differentiate between them based on their names and descriptions.
Both tools follow a consistent verb_noun pattern with get_assets and get_risk_score. The naming is predictable and readable, using snake_case throughout without any deviations or mixed conventions.
With only 2 tools, the server feels thin for a BICScan MCP Server, which suggests a domain involving crypto and risk analysis. This limited set may not cover essential operations like querying transactions, checking contract details, or updating data, making it borderline inadequate for the apparent scope.
The tool surface has significant gaps for a crypto/risk analysis domain. While it includes asset retrieval and risk scoring, it lacks basic operations such as querying transaction histories, verifying contract addresses, or performing deeper analytics. This incompleteness could lead to agent failures when handling common tasks in this domain.