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qinisolabs

sanctionwise

by qinisolabs
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Server Quality Checklist

83%
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  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: screen_name screens names against the sanctions list, while get_sanctions_entry retrieves full details for a specific entry by ID. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using snake_case: screen_name and get_sanctions_entry. This makes the action and target immediately clear.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped for its purpose of sanctions screening and detail retrieval. It is slightly thin, but each tool serves a critical and distinct function.

    Completeness4/5

    The server covers the two primary actions needed for sanctions list interaction: screening a name and fetching a full entry. Missing tools like listing entries or regimes are minor gaps, but the core workflow is supported.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • 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. It discloses that the tool fetches data and lists fields, but does not mention behavioral traits like read-only nature, authentication needs, rate limits, or error handling. It is minimally adequate.

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

    Conciseness5/5

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

    The description is a single sentence, front-loaded with 'USE THIS', and efficiently conveys all necessary information without redundancy.

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

    Completeness4/5

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

    Given no output schema, the description adequately explains what the tool returns (full entry with listed fields). It could mention output format or any limitations, but for a simple fetch tool, it is sufficiently complete.

    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 already describes the 'id' parameter with an example. The description adds value by noting that the ID can come from 'screen_name', providing operational context that the schema lacks.

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

    Purpose5/5

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

    The description clearly states the verb 'fetch', the resource 'full official UK Sanctions List entry', and distinguishes from the sibling tool 'screen_name' by mentioning IDs returned by it. It also lists the data fields included, making the purpose very specific.

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

    Usage Guidelines4/5

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

    The description explicitly says 'USE THIS' and provides context that the ID can come from 'screen_name', linking it to the sibling. However, it does not explicitly state when not to use or give alternatives beyond that.

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

  • Behavior5/5

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

    No annotations provided, so description bears full burden. It discloses behavior: returns ranked possible matches with designation details, indicative only, must be verified, covers only UK list, string-based, data date, and not legal advice.

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

    Conciseness5/5

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

    Front-loaded with the critical usage instruction, then provides essential details, warnings, and disclaimers. Every sentence adds value with no redundancy.

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

    Completeness5/5

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

    Given the complexity of sanctions screening and lack of output schema, the description fully explains the output (ranked matches, details), limitations (indicative, not clearance), and dataset scope. No 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?

    Schema coverage is 100% and schema descriptions are present. The tool description adds context by linking 'name' to person/company/vessel and 'limit' to number of matches, reinforcing their purpose in the screening workflow.

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

    Purpose5/5

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

    The description explicitly states the tool screens a name against the UK Sanctions List, using the verb 'screen' and specifying the resource. It distinguishes itself from the sibling tool 'get_sanctions_entry' by focusing on proactive screening.

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

    Usage Guidelines5/5

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

    Clear when-to-use ('before onboarding, paying, or transacting') and when-not-to-use: warnings that a match is not confirmation, no match is not clearance, and it covers only the UK FCDO list. Explicitly directs to human verification.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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