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alejamorenovallejo

VISA Dispute Advisor

by alejamorenovallejo
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve completely distinct purposes: one searches Visa dispute rules, the other retrieves merchant dispute history. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern in snake_case: 'search_visa_rules' and 'query_warehouse'. The naming is predictable and uniform.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a 'VISA Dispute Advisor'. One would expect additional tools for creating, updating, or resolving disputes to make it useful.

    Completeness2/5

    The tool set lacks core dispute management capabilities like creating a dispute, updating status, or listing disputes. Only rule search and merchant history are provided, leaving significant gaps.

  • Average 4.1/5 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

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  • 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?

    No annotations are provided; description carries full burden. Describes return format but does not disclose behavioral traits such as read-only nature, side effects, authentication needs, or rate limits. For a search tool, this is a notable gap.

    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?

    Well-structured with Args and Returns sections. Every sentence is informative and earns its place. Front-loaded with the main purpose. No wasted words.

    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 simple schema (1 param) and no annotations, the description is fairly complete: explains input and output format. Lacks details on behavior (e.g., read-only) but covers essential usage info. Output schema exists, so return values are partially covered.

    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 0%, so description compensates well with detailed explanation of 'scenario' parameter: 'Free-text description of the dispute (cardholder claim, merchant response, transaction type, etc.).' Adds meaning beyond the schema but lacks details on length or format.

    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?

    Description clearly states the tool searches VISA Dispute Management Guidelines for rules relevant to a dispute. Uses specific verb 'search' and resource 'VISA rules'. Distinguishes from sibling query_warehouse by being specialized for visa disputes.

    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 context: use when searching for VISA rules for a dispute. Args section explains input scenario. Does not explicitly state when not to use or name alternatives, but the specialization is clear.

    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?

    No annotations are provided, so the description carries the full burden. It implies a read-only operation by using 'retrieve', but does not explicitly state that it is safe, idempotent, or any potential side effects. The return structure is described, which adds some context.

    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 concise and well-structured with clear Args and Returns sections. Every sentence adds value, and the most important info (purpose) is front-loaded.

    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?

    For a tool with one required parameter and an explicit return structure described, the description is fairly complete. However, it lacks details on error handling, pagination limits, or authorization requirements, which would be beneficial for a complete understanding.

    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 coverage is 0%, meaning the input schema provides only type string with no description. The description compensates fully by naming the parameter, providing an example value ('MER-001'), and explaining its purpose (merchant identifier).

    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 it retrieves dispute history for a merchant from a data warehouse, with specific verb 'retrieve' and resource 'dispute history'. It distinguishes from sibling tool 'search_visa_rules' which addresses a different domain.

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

    Usage Guidelines3/5

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

    The description explains the parameter merchant_id with an example but does not explicitly state when to use this tool versus alternatives, nor provide any exclusion criteria. Sibling tool name implies different use case, but no direct guidance is given.

    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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