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twodoorsdev

React Native Debugger MCP

by twodoorsdev
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves a list of connected apps, while the other reads console logs from a specific app. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency4/5

    The tool names follow a consistent verb_noun pattern (getConnectedApps, readConsoleLogsFromApp), with both using camelCase. However, the second tool name is quite long and descriptive, which slightly deviates from the simplicity of the first, but the overall naming convention is clear and predictable.

    Tool Count2/5

    With only two tools, the server feels thin for a React Native debugger domain, which typically involves more operations like inspecting elements, setting breakpoints, or managing app state. This limited set may not cover essential debugging workflows, indicating an under-scoped tool surface.

    Completeness2/5

    The tool set is severely incomplete for a debugger server. It lacks basic debugging operations such as inspecting app components, setting breakpoints, evaluating expressions, or controlling app execution. This creates significant gaps that will likely cause agent failures in comprehensive debugging tasks.

  • Average 3.1/5 across 2 of 2 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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

  • 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 of behavioral disclosure. 'Get' implies a read operation, but the description doesn't specify permissions, rate limits, or what 'connected apps' entails (e.g., format, scope). It lacks critical behavioral details for a tool with no annotation coverage.

    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 a single, efficient sentence with no wasted words, making it appropriately sized. However, it's front-loaded with minimal content, which could be seen as under-specified rather than optimally concise.

    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 no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It doesn't explain what 'connected apps' are, the return format, or behavioral aspects, leaving significant gaps for the agent to understand the tool's context and 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?

    The input schema has 100% description coverage, with the parameter 'metroServerPort' clearly documented. The description adds no parameter semantics beyond the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Get the connected apps' states a clear verb ('Get') and resource ('connected apps'), but it's vague about what 'connected apps' means in this context and doesn't distinguish it from the sibling tool 'readConsoleLogsFromApp'. It provides a basic purpose but lacks specificity.

    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?

    No guidance is provided on when to use this tool versus the sibling 'readConsoleLogsFromApp' or any alternatives. The description offers no context, prerequisites, or exclusions, leaving the agent with no usage direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    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 mentions the action is a read operation and uses a WebSocket, but lacks details on permissions, rate limits, error handling, or what the logs contain (e.g., format, timestamps). This is inadequate for a tool that interacts with a debugger.

    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 that front-loads the core purpose without unnecessary words. Every part of the sentence contributes essential information, making it highly concise and well-structured.

    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 complexity of interacting with a debugger WebSocket, no annotations, and no output schema, the description is incomplete. It does not explain return values (e.g., log format), error conditions, or prerequisites like needing an active debugger connection, leaving significant gaps for an AI agent.

    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 fully documents both parameters ('app' and 'maxLogs'). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining how 'app' should be obtained or typical values for 'maxLogs.' Baseline 3 is appropriate here.

    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 ('Reads console logs') and the resource ('from a connected React Native app'), including the mechanism ('through the debugger WebSocket'). It distinguishes itself from the sibling tool 'getConnectedApps' by focusing on log retrieval rather than app listing.

    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?

    The description implies usage context by specifying 'from a connected React Native app,' suggesting this tool should be used after obtaining app information (likely from 'getConnectedApps'). However, it does not explicitly state when not to use it or name alternatives, which prevents a perfect score.

    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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