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guardd

Orcho MCP Server

by guardd
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JavaScript
Remote

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 'assess_risk' has a single, clearly defined purpose: to evaluate the risk level of coding prompts using the Orcho API. This eliminates any ambiguity in tool selection.

    Naming Consistency5/5

    The naming follows a consistent verb_noun pattern with 'assess_risk', which is clear and descriptive. Since there is only one tool, there is no inconsistency or mixing of conventions to evaluate, making it perfectly consistent by default.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and can feel thin. For a risk analysis server, one tool might suffice if it covers all necessary operations, but typical MCP servers benefit from multiple tools to handle different aspects of a domain, making this count borderline inadequate.

    Completeness3/5

    The tool 'assess_risk' provides a core risk assessment function, but there are notable gaps in coverage. For a risk analysis domain, one might expect additional tools for managing risk profiles, retrieving historical assessments, or configuring risk thresholds. However, the single tool does address the primary need, leaving room for expansion.

  • Average 4.3/5 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.

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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. It effectively discloses key behavioral traits: the tool's dependency on context for enhanced analysis ('Without context, only basic risk assessment is available'), the importance of editor state access, and the specific outputs enabled by context ('blast radius and complexity analysis'). It doesn't mention rate limits or authentication needs, but covers the core operational behavior well.

    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 appropriately sized and front-loaded: the first sentence states the purpose, followed by critical usage instructions. Every sentence earns its place by providing essential guidance. It could be slightly more concise by integrating some details, but overall it's well-structured and efficient.

    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 (6 parameters, nested objects) and no annotations or output schema, the description does a good job of explaining how to use it effectively. It covers the importance of context, parameter usage, and the enhanced analysis available. It doesn't describe the return format, but with no output schema, this is a minor gap rather than a critical omission.

    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 100%, so the schema already documents all parameters thoroughly. The description adds some value by emphasizing the importance of 'current_file' and 'other_files' for context-aware assessment, but doesn't provide additional semantic details beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

    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 tool's purpose: 'Assess the risk level of your coding prompt using Orcho risk analysis API.' It specifies the verb ('assess'), resource ('risk level of your coding prompt'), and method ('using Orcho risk analysis API'). With no sibling tools, this level of specificity is excellent.

    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?

    The description provides explicit usage guidance: 'CRITICAL: You (Cursor AI) have access to the editor state - ALWAYS include context when available.' It details when to use specific parameters (current_file when available, other_files based on prompt analysis) and explains the benefits of context ('With context, you get blast radius and complexity analysis').

    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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