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Mtar786

Example MCP Server

by Mtar786

Server Quality Checklist

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

  • Disambiguation5/5

    The two tools have completely distinct purposes: one performs a mathematical addition operation on two numbers, while the other retrieves the current server time. There is no overlap or ambiguity between these functions, making it impossible for an agent to confuse them.

    Naming Consistency2/5

    The naming conventions are inconsistent: 'add' uses a simple verb form, while 'getTime' uses camelCase with a verb-object structure. This mixing of styles (no separator vs. camelCase) lacks a predictable pattern, though the names are still readable.

    Tool Count2/5

    With only two tools, the server feels thin and under-scoped for a general-purpose MCP server. This minimal set suggests limited functionality that might not cover basic workflows or domain needs effectively, making it borderline too few for practical use.

    Completeness1/5

    The tool surface is severely incomplete; it lacks any coherent domain coverage. The tools are unrelated (math and time), with no CRUD operations, lifecycle management, or logical connections, leaving significant gaps that would cause agent failures in most scenarios.

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

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

  • 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

  • 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 only states the basic function without mentioning error handling, precision limits, or output format, leaving significant gaps for a tool that performs a mathematical operation.

    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 and front-loaded in a single sentence with zero waste. Every word directly contributes to understanding the tool's purpose without unnecessary elaboration.

    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 lack of annotations, output schema, and low schema description coverage, the description is incomplete. It doesn't address behavioral aspects like error cases or result format, making it inadequate for a tool with two parameters and no structured support.

    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 0%, but the description implies the parameters are numbers to be added. It doesn't specify parameter roles or constraints beyond what the schema's type hints provide, resulting in a baseline score due to minimal added value.

    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 ('Add') and resource ('two numbers'), making it immediately understandable. However, it doesn't distinguish from sibling tools (like 'getTime'), which prevents 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 or in what context it should be applied. It simply states what it does without any usage context or exclusions.

    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 full burden. It states what the tool does ('Get the current server time') but doesn't disclose behavioral traits like whether this requires authentication, has rate limits, returns a specific format, or has any side effects. For a tool with zero annotation coverage, this is a significant gap in behavioral transparency.

    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 wasted words. It's front-loaded with the core purpose and appropriately sized for this simple tool. Every word earns its place.

    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 simplicity (0 parameters, no annotations, no output schema), the description is minimally complete—it states what the tool does. However, without annotations or output schema, it lacks information about return format or behavioral constraints that would be helpful for an agent. It's adequate but has clear gaps for a tool that returns data.

    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?

    The tool has 0 parameters with 100% schema description coverage (empty schema). The description doesn't need to explain parameters since there are none, and it correctly doesn't mention any. This exceeds the baseline of 3 for high schema coverage by being perfectly appropriate for a parameterless tool.

    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 'Get the current server time' clearly states the verb ('Get') and resource ('current server time'), making the purpose immediately understandable. It's not tautological with the name 'getTime' since it specifies 'server time' rather than just 'time'. However, it doesn't differentiate from sibling tools beyond the obvious domain difference.

    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 about when to use this tool versus alternatives. While the sibling tool 'add' appears unrelated (likely mathematical), there's no explicit comparison or context about when this time-fetching operation is appropriate versus other time-related operations that might exist in a broader context.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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