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ramaiyaKushal

AVA MCP Server

by ramaiyaKushal
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clearly defined as creating email drafts via Gmail API.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (write_email_draft). With only one tool, consistency is inherently perfect as there are no other names to compare against.

    Tool Count2/5

    A single tool is insufficient for an email/Gmail server's scope. Basic email operations like sending, reading, listing, or deleting emails are missing, making this server feel incomplete and thin for its apparent domain.

    Completeness2/5

    The server is severely incomplete for email management. While write_email_draft covers draft creation, it lacks essential operations like send_email, list_emails, get_email, delete_email, or manage labels, creating significant gaps that will cause agent failures.

  • Average 3.9/5 across 1 of 1 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.

  • 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the operation's purpose, return values (success dictionary with id/message or None), error conditions (HttpError), and prerequisites (credentials, environment variable, permissions). It doesn't mention rate limits, retry behavior, or concurrency considerations, but covers the essential behavioral aspects for a write operation.

    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 well-structured with clear sections (Args, Returns, Raises, Note) and efficiently conveys necessary information. While slightly longer than minimal, each section earns its place by providing valuable context. The information is front-loaded with the core purpose stated first.

    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 the tool's complexity (write operation with API dependencies), no annotations, and no output schema, the description does a good job of providing context. It explains the operation, parameters, return values, errors, and prerequisites. While it could mention more about the Gmail API context or provide examples, it covers the essential information needed to understand and use the tool.

    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?

    With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It provides clear parameter descriptions in the Args section, explaining what each parameter represents (recipient_email, subject, body). While it doesn't specify format constraints or examples, it adds substantial meaning beyond the bare schema.

    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 specific action ('Create a draft email') and resource ('using the Gmail API'), with no sibling tools to differentiate from. It provides a complete verb+resource+scope statement that leaves no ambiguity about what the tool does.

    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. While it mentions prerequisites (Gmail API credentials, environment variable, permissions), it doesn't indicate scenarios where this tool is appropriate or when other email-related tools might be preferred. No explicit when/when-not/alternatives information is provided.

    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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  • Evaluate tool definition quality.

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