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AlexanderLawson17

revettr

by AlexanderLawson17
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    With only a single tool available, there is no possibility of overlap or confusion with other tools. The 'score_counterparty' tool has a unique, specific purpose with no alternatives to misselect.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern ('score_counterparty'). With only one tool in the set, there are no naming convention inconsistencies to evaluate.

    Tool Count3/5

    A single tool represents a minimal surface that feels thin for a financial risk assessment domain. While it covers the core scoring action, the lack of supporting tools (e.g., retrieving historical scores, listing past screenings) limits workflow flexibility.

    Completeness3/5

    The tool covers the primary 'score' operation but lacks complementary lifecycle operations such as retrieving previous scores, listing screening history, or batch processing. Notable gaps exist for audit trails and historical verification workflows.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • 0 of 2 community issues answered or closed 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 is passing

  • This repository is licensed under MIT License.

  • 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.

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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

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses the risk assessment nature, mentions sanctions list screening (OFAC/EU/UN), and details the return structure including score range (0-100) and response components. Could clarify if this makes external API calls or has side effects, but adequately covers the core behavioral traits.

    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?

    Well-structured with clear sections (purpose, constraints, usage, args, returns). Front-loaded with critical constraints. Slightly verbose due to long wallet address examples, but every section provides 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 6 parameters with complex financial domain concepts and existing output schema, the description is nearly complete. It covers input requirements, validation rules, and return value structure. Could elaborate on risk tier meanings or confidence scoring methodology, but sufficient for agent operation.

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

    Parameters5/5

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

    Schema description coverage is 0%, but the description fully compensates with an 'Args:' section documenting all 6 parameters with semantic meanings (e.g., company_name screens 'against OFAC/EU/UN sanctions lists') and concrete examples for each. This significantly exceeds the baseline requirement given the schema deficiency.

    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?

    Opens with specific verb 'Score' + resource 'counterparty' + context 'before sending money', clearly defining the tool's function. No siblings exist to confuse with, but the description establishes a clear, specific purpose.

    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?

    Provides explicit constraints ('Only use data explicitly provided...', 'Do not fabricate input values'), explains the flexible input pattern ('Send any combination... At least one field is required'), and specifies when to use ('before sending money'). Lacks explicit 'when not to use' or alternatives, but none exist in this server.

    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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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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