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v4lheru

generic-mcp-server

by v4lheru
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TypeScript

Server Quality Checklist

50%
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: calculate-sum performs a mathematical operation on numbers, while get-weather retrieves weather data for a city. There is no overlap or ambiguity between these functions, making it impossible for an agent to confuse them.

    Naming Consistency3/5

    The tool names use a verb-noun pattern (calculate-sum, get-weather), which is consistent in structure. However, they use different verb styles (calculate vs. get) and a hyphen delimiter, which is readable but shows minor deviation in convention.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a generic purpose. This minimal set lacks coverage for typical operations in any specific domain, making it inappropriate for most agent workflows.

    Completeness1/5

    The tool surface is severely incomplete for a generic server. There are major gaps in functionality, such as missing basic CRUD operations, data processing tools, or domain-specific coverage, which will likely cause agent failures in practical use.

  • Average 3/5 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

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

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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 mentions 'Get weather forecast' but doesn't specify details like data freshness, rate limits, error handling, or authentication needs. 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 purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

    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 and output schema, the description is incomplete. It doesn't explain what the weather forecast includes (e.g., temperature, conditions), potential limitations, or return format. For a tool with no structured behavioral data, more context is needed to fully understand its operation.

    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 'city' parameter fully documented. The description implies the parameter's use ('for a city') but adds no additional meaning beyond what the schema provides, such as format examples or constraints. This meets the baseline for high schema coverage.

    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 ('Get') and resource ('weather forecast for a city'), making it immediately understandable. However, it doesn't differentiate from sibling tools, as 'calculate-sum' is unrelated, so no sibling distinction is needed or provided.

    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, such as other weather-related tools or data sources. It only states what the tool does, without context for its application or prerequisites.

    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. While 'calculates' implies a read-only operation, the description doesn't address important behavioral aspects like error handling (e.g., overflow, invalid inputs), performance characteristics, or whether the operation has side effects. For a tool with zero annotation coverage, this represents a significant gap in behavioral context.

    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 perfectly concise at just one sentence that directly states the tool's function. There's zero wasted language, no unnecessary elaboration, and the information is front-loaded effectively. Every word earns its place in this minimal but complete statement of purpose.

    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 (simple arithmetic operation), 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks information about return values (though no output schema exists), error conditions, or limitations (e.g., numeric range constraints). For a basic calculation tool, this represents the minimum viable description but with clear gaps in completeness.

    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%, with both parameters 'a' and 'b' clearly documented as 'the first number' and 'the second number' respectively. The description adds no additional parameter semantics beyond what the schema already provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

    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 ('calculates') and resource ('sum of two numbers'). It distinguishes from the sibling tool 'get-weather' by focusing on mathematical calculation rather than weather data retrieval. However, it doesn't explicitly differentiate from potential mathematical sibling tools, 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. There's no mention of when-not-to-use scenarios or comparison with other calculation tools. The only implied usage is for summing two numbers, but this is basic and doesn't help an agent make informed decisions about tool selection.

    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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