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TypeScript MCP Server Boilerplate

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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct and non-overlapping purpose: calculator for arithmetic, greeting for language-based salutations, and image_generation for AI-based visual creation. There is no ambiguity in tool selection as they target completely different domains and use cases.

    Naming Consistency3/5

    The naming is mixed with 'calculator' and 'greeting' as nouns and 'image_generation' as a verb_noun pattern, lacking a uniform convention. However, all names are readable and descriptive, avoiding chaotic styles like camelCase or abbreviations.

    Tool Count2/5

    With only 3 tools, the set feels thin and under-scoped for a 'TypeScript MCP Server Boilerplate,' which typically implies a broader utility or domain coverage. The tools are disparate and do not form a coherent surface for any specific purpose, making the count too low for effective agent workflows.

    Completeness2/5

    The tool set is severely incomplete for a boilerplate server, lacking core operations like CRUD, configuration, or domain-specific functions. There are significant gaps as the tools are unrelated and do not cover a coherent domain, leading to potential agent failures in structured tasks.

  • Average 3/5 across 3 of 3 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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. The description only states what the tool does ('perform basic arithmetic operations') without mentioning any behavioral traits like error handling (e.g., division by zero), precision limitations, or output format. This leaves significant gaps for a tool that performs calculations.

    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 extremely concise with a single, clear sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it front-loaded and efficient.

    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 (performing calculations with potential edge cases) and the absence of both annotations and an output schema, the description is incomplete. It doesn't address behavioral aspects like error handling or result formatting, leaving the agent with insufficient context for reliable use.

    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 schema description coverage is 100%, with all parameters well-documented in the input schema. The description adds no additional meaning beyond what the schema provides, as it doesn't elaborate on parameter usage or constraints. This meets the baseline score when schema coverage is high.

    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 with a specific verb ('perform') and resource ('basic arithmetic operations'). It distinguishes from siblings like 'greeting' and 'image_generation' by focusing on mathematical calculations. However, it could be more specific about what constitutes 'basic' operations.

    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. There are no explicit instructions about when to use it, when not to use it, or what alternatives might exist. The agent must infer usage solely from the purpose statement.

    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 AI-based generation but lacks details on traits like rate limits, quality variations, processing time, or potential costs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

    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 directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, making it easy to understand at a glance.

    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?

    For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the output looks like (e.g., image format, size), potential errors, or usage constraints. Given the complexity of AI image generation, more context is needed for effective use.

    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 'prompt' parameter clearly documented. The description adds no additional meaning beyond what the schema provides, such as prompt formatting tips or examples. Given the high schema coverage, a baseline score of 3 is appropriate.

    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 action ('Generate an image') and the resource ('from a text prompt using AI'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'calculator' or 'greeting', which is unnecessary since they serve completely different functions, so a 5 is not warranted.

    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 or in what context it should be applied. It simply states what it does without indicating any prerequisites, limitations, or scenarios where it might be preferred over other methods.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's function but doesn't describe what happens when invoked—such as whether it returns a string, logs the greeting, or has side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

    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 with the core purpose and includes essential context about language specification. Every word earns its place, 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.

    Completeness3/5

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

    Given the tool's low complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior and usage context. Without annotations or output schema, more behavioral transparency would improve completeness for agent invocation.

    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 both parameters ('name' and 'language') with descriptions and an enum for 'language'. The description adds no additional parameter semantics beyond implying language customization. Baseline 3 is appropriate when the schema handles most of the documentation.

    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 verb ('Greet') and resource ('a user'), specifying the action and target. It adds meaningful context about language customization, which distinguishes it from generic greeting tools. However, it doesn't explicitly differentiate from sibling tools like 'calculator' or 'image_generation' since those are unrelated functions.

    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. It doesn't mention any prerequisites, constraints, or scenarios where this tool is preferred over other methods of greeting. With unrelated sibling tools, explicit differentiation isn't needed, but general usage context is missing.

    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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