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jsmejia78

mcp-server

by jsmejia78
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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a completely different domain: dice rolling, stock info, and web search. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    All names use snake_case, but 'roll_dice' follows a verb_noun pattern while 'stock_info' and 'web_search' are noun_noun. This is a minor inconsistency but still readable.

    Tool Count4/5

    With 3 tools, the set is small but still feels reasonable for a general-purpose utility server. It is not excessively thin for the apparent scope.

    Completeness2/5

    The tools cover three unrelated functionalities with no common domain. There is no coverage of typical CRUD or lifecycle operations, and the set seems arbitrarily assembled without a clear purpose.

  • Average 2.8/5 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.

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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.

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

    Then . Browse examples.

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

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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

  • Behavior1/5

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

    No annotations provided, yet the description discloses no behavioral traits beyond the basic action. No mention of safety, limitations, rate limits, or output nature. Description carries full burden but adds no value.

    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?

    One efficient sentence, front-loaded with the action. No wasted words. However, conciseness sacrifices necessary detail.

    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 simplicity and presence of output schema, the description is minimal. It lacks context about result format, scope, or limitations. More context would help an agent form appropriate expectations.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, requiring compensation from the description. The description only uses 'the given query' which adds no semantic meaning beyond the parameter name and type. Fails to specify query format, expected input, or usage constraints.

    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 'Search the web for information' with a specific verb and resource. It distinguishes itself from sibling tools 'roll_dice' and 'stock_info' which are for different tasks.

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

    Usage Guidelines1/5

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

    No guidance on when to use this tool vs alternatives. No best practices, prerequisites, or when-not-to-use. The description is purely functional.

    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 provided, so the description bears full burden. It implies a read-only operation ('Get'), but does not disclose behavioral traits such as rate limits, data freshness, or potential side effects. The term 'consolidated' hints at aggregation but is insufficient.

    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 sentence with no wasted words. It is appropriately front-loaded and efficient. However, it could be slightly more informative without sacrificing conciseness (e.g., mention that it returns price and fundamentals).

    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 has one parameter, no annotations, and an output schema (not provided), the description is too sparse. It does not explain the output structure, data types, or any limitations. For a simple tool, more completeness is expected to aid correct invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The single parameter 'ticker' has 0% schema description coverage. The description adds only that it refers to 'a stock TICKER', clarifying it's a stock symbol. This adds marginal value beyond the schema field name. More detail on format or allowed values would improve semantic clarity.

    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 'Get' and resource 'consolidated stock information about a stock TICKER'. It distinguishes from sibling tools (roll_dice, web_search) by content type. However, 'consolidated' is vague—does it include price, fundamentals, news? Slightly ambiguous but still clear.

    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 on when to use this tool versus alternatives. The description does not specify context (e.g., retrieving real-time vs. historical data) or exclusions (e.g., only supports US stocks). It simply states what it does without usage direction.

    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?

    Annotations are absent, so the description must convey behavioral traits. It implies a random simulation but does not explicitly state that it generates random results, nor does it mention any side effects or constraints. The presence of an output schema is not referenced. This is adequate for a simple tool but lacks thoroughness.

    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, concise sentence with no wasted words. However, it is too brief given the lack of parameter details and behavioral context, missing opportunities to add value while remaining 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 simplicity of the tool and the existence of an output schema, the description is incomplete. It does not explain the notation format, handling of multiple rolls, or what the tool returns. Essential details for correct invocation are missing.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description provides no explanation of parameters. It does not clarify what 'notation' means (e.g., '2d6+3') or the role of 'num_rolls'. This forces the agent to rely solely on the schema, which has no descriptions for the properties.

    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 action ('Roll the dice') and the input ('with the given notation'), making the tool's purpose immediately understandable. It is a specific verb+resource pair that distinguishes it from unrelated sibling tools like stock_info and web_search.

    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 alternatives, nor any prerequisites or context. While siblings are unrelated, the description lacks any indication of appropriate scenarios or exclusions.

    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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  • 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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