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ahnlabio

BICScan MCP Server

by ahnlabio
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count2/5

    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.

    Completeness2/5

    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.

  • Average 3.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'Get' operation which implies read-only behavior, but doesn't disclose any behavioral traits like rate limits, authentication requirements, error conditions, or what happens with invalid addresses. The description mentions the return format ('Dict: where assets is a list of assets') but provides minimal detail about the response structure.

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

    Conciseness4/5

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

    The description is appropriately sized and well-structured with clear sections for Args and Returns. The main purpose is stated upfront, followed by parameter details and return information. There's minimal wasted text, though the formatting could be slightly cleaner (e.g., consistent capitalization in the Returns section).

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

    Completeness3/5

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

    Given the tool's moderate complexity (single parameter with multiple formats), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameter semantics well, but lacks behavioral context and usage guidance. For a tool that presumably queries blockchain data, more information about limitations, data freshness, or error handling would be beneficial.

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

    Parameters4/5

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

    The description adds significant value beyond the input schema, which has 0% description coverage. It explains that the 'address' parameter accepts multiple formats: 'EOA, CA, ENS, CNS, KNS' (presumably Externally Owned Account, Contract Address, Ethereum Name Service, etc.). This semantic clarification is crucial for proper tool invocation and compensates well for the schema's lack of documentation.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Get Assets holdings by CryptoAddress' specifies both the action (get) and the resource (assets holdings). It distinguishes from the sibling tool 'get_risk_score' by focusing on asset holdings rather than risk assessment. However, it doesn't fully differentiate from potential other asset-related tools beyond the single sibling.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate, what scenarios it's designed for, or any prerequisites for its use. The single sibling tool 'get_risk_score' is not referenced, leaving the agent with no comparative context.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context by explaining the return format and risk score range (0-100, where 100 is high risk), which is useful beyond basic functionality. However, it lacks details on error handling, rate limits, or authentication needs, leaving gaps in behavioral understanding.

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

    Conciseness4/5

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

    The description is appropriately sized and front-loaded, with the purpose stated first, followed by parameter and return details. It avoids unnecessary fluff, but the formatting with 'Args:' and 'Returns:' could be more integrated for better flow.

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

    Completeness3/5

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

    Given the tool's complexity (risk scoring with various address types), no annotations, and no output schema, the description is moderately complete. It covers purpose, parameter semantics, and return format, but lacks usage guidelines, error details, or behavioral traits like rate limits, making it adequate but with clear gaps.

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

    Parameters4/5

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

    The schema description coverage is 0%, so the description must compensate. It adds meaning by listing examples of address types (EOA, CA, ENS, CNS, KNS, HostName), which clarifies the parameter's semantics beyond the schema's generic 'string' type. This significantly enhances understanding, though it could be more detailed on format constraints.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Get Risk Score for Crypto, Domain Name, ENS, CNS, KNS or even Hostname Address.' It specifies the verb 'Get' and the resource 'Risk Score,' with explicit examples of address types. However, it doesn't differentiate from the sibling tool 'get_assets,' which might have overlapping functionality, preventing a perfect score.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, such as the sibling tool 'get_assets.' It lists address types but doesn't specify contexts or exclusions for usage, leaving the agent without clear direction on tool selection.

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

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