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askarthikey

MCP Starter Server

by askarthikey

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    add_numbers and get_system_status have completely separate purposes, so an agent would not confuse them. Each tool's name and description uniquely identify its intended action.

    Naming Consistency5/5

    Both tools use a consistent verb_noun snake_case pattern: add_numbers and get_system_status. This makes the naming predictable and easy to extend.

    Tool Count3/5

    Two tools is on the low end and feels thin for an MCP server. However, for a starter server it is defensible, so the count is borderline rather than unreasonable.

    Completeness4/5

    The two tools cover what a minimal starter server would likely demonstrate: a computational example and a system information example. There are no obvious missing operations given the small scope, though the surface is not broad enough for general-purpose use.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    There are no annotations, so the description carries the full burden. It clearly indicates a read-only fetch behavior, which is useful, but it does not disclose whether the returned status is a plain object, whether errors or empty values are possible, or any environment-specific caveats. Given the simple nature of a status query, this is adequate but not thorough.

    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, front-loaded sentence that immediately states the action and lists the relevant content. There is no filler or repetition of the tool name.

    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?

    With no output schema or annotations, the description provides enough grounding about the expected contents, including four concrete status fields. It stops short of describing the exact return shape or exception behavior, but for a zero-parameter status query this is a reasonable, workable definition.

    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?

    There are 0 parameters, so there is no parameter semantics to explain. The description compensates by enumerating the kind of data that will be returned, which helps clarify what 'status' means even without an output schema.

    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 uses a specific verb ('Retrieves') and names the resource ('system runtime status') along with concrete fields: Node version, OS, uptime, and timestamp. This clearly distinguishes it from sibling add_numbers, though it does not explicitly name the sibling as an alternative.

    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 given for when to use this tool versus alternatives or when not to use it. While the sibling 'add_numbers' is clearly unrelated, there is no explicit usage context or exclusion criteria.

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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the full behavioral burden. It discloses the core behavior and the fact that a result is returned, which is adequate for a pure arithmetic function. However, it says nothing about edge cases, error handling, or whether the operation is stateless — though the simplicity lessens the severity of these omissions.

    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?

    One sentence, ten words, zero fluff. The action verb and resource are front-loaded and every word earns its place. There is nothing extraneous.

    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?

    The tool is trivial in complexity, so the schema's thorough parameter docs and the description's clear operation statement are largely sufficient. Could theoretically note edge cases or the return type explicitly, but the implied number return is practically unambiguous.

    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 already provides 100% coverage, so the baseline of 3 applies. The description adds no extra meaning about the parameters beyond what the schema states — it simply confirms the operation performed on them.

    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?

    Names a specific verb ('adds') and resource ('two numbers') and goes beyond a mere label by stating the outcome ('returns the result'). The sibling tool get_system_status is so categorically different that there is no realistic ambiguity about which tool this is.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The intended use is obvious for the operation being performed, but no guidance is given for distinguishing when to use this over an alternative. There is no when-to-use or when-not-to-use context — the usage is implied but never stated.

    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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