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danielsebastianc

frappe-api-mcp

by danielsebastianc
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JavaScript

Server Quality Checklist

50%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clearly defined purpose of calling any Frappe REST API endpoint, leaving no room for confusion or misselection.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'frappe_api' follows a clear and appropriate pattern that matches the server's purpose, with no other tools to compare against or create inconsistency.

    Tool Count2/5

    A single tool is too few for a server with the broad scope implied by 'frappe-api-mcp', which suggests a comprehensive API interface. While the tool is flexible, the lack of specialized tools for common operations (e.g., CRUD on specific resources) makes the surface feel thin and underdeveloped for the domain.

    Completeness2/5

    The tool set is severely incomplete for the apparent domain of Frappe API interactions. With only a generic 'call any endpoint' tool, there are significant gaps in coverage—no dedicated tools for common operations like creating, reading, updating, or deleting specific resources, which will likely cause agent failures due to lack of structured guidance.

  • Average 2.9/5 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

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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, so the description carries the full burden. It mentions token authentication but doesn't disclose rate limits, error handling, response formats, or side effects (e.g., that DELETE methods are destructive). For a tool that can perform any REST operation, this lack of behavioral context is inadequate.

    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, efficient sentence with zero waste. It's front-loaded and appropriately sized for its purpose, making it easy to parse quickly.

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

    Completeness2/5

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

    Given the tool's complexity (5 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain return values, error cases, or the scope of 'any Frappe REST API endpoint'. For a flexible, potentially destructive tool, more context is needed to ensure safe and correct usage.

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

    Parameters3/5

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

    Schema description coverage is 80%, so the schema documents most parameters well. The description adds no parameter-specific information beyond implying the path is under '/api'. It doesn't explain parameter interactions or provide examples, so it meets the baseline but doesn't compensate for the 20% coverage gap.

    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: 'Call any Frappe REST API endpoint via /api using configured token authentication.' It specifies the verb ('Call'), resource ('Frappe REST API endpoint'), and authentication method. However, without sibling tools, it cannot distinguish from alternatives, so it doesn't reach 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, prerequisites, or specific contexts. It only states what the tool does, not when it's appropriate. This is a significant gap for a general-purpose API tool.

    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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