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oleksii-donets

simple_mcp

by oleksii-donets
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, random_float and wait, have completely distinct purposes—one generates a random number and the other introduces a delay. There is no ambiguity whatsoever.

    Naming Consistency4/5

    Both names are in lowercase snake_case, but random_float is a noun phrase while wait is a verb, creating a minor inconsistency in style. However, with only two tools, the names are clear and identifiable, so the deviation is negligible.

    Tool Count3/5

    The server exposes only two tools, which feels thin for a general-purpose utility server. However, the scope itself is extremely narrow (random number and delay), so the count is borderline—not excessive, but minimal for broader use cases.

    Completeness3/5

    The domain appears to be basic utility operations, but with only two tools, there are obvious gaps such as random integer generation or more granular timing controls. The surface is too minimal to consider it complete for any meaningful utility purpose.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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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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?

    With no annotations, the description carries the transparency burden. It clearly states that the tool waits for the specified time and returns a message, which is the core behavior. However, it does not disclose details such as whether the operation is synchronous, potential error conditions, or the exact content of the returned message.

    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 conveys the essential purpose without any fluff. It is concise and well-structured.

    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?

    For such a simple tool with one parameter and an output schema, the description is largely complete. It explains the action and the return of a message. It does not detail the message content, but the output schema likely covers that. Minor gaps are acceptable given the simplicity.

    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 description explicitly clarifies that the parameter is in milliseconds, which adds meaning beyond the schema's property name and type. This is particularly helpful given the schema description coverage is 0%.

    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 verb 'Wait' and the resource 'specified time in milliseconds', and specifies that it returns a message. It is specific and distinguishes itself from the sibling tool 'random_float', which has a completely different purpose.

    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, nor any preconditions or exclusions. It only states what the tool does without contextualizing it.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of explaining behavior. It clearly states the stochastic nature ('random') and the return interval '[0,1)' though it does not explicitly mention uniformity or other implementation details. This is adequate for a simple, stateless utility.

    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?

    A single, concise sentence is used—'Return a random float in [0,1).' It is front-loaded, contains no filler or redundant wording, and is exactly as long as needed.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

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

    For a zero-parameter tool with an output schema, the description fully specifies the result with no ambiguities. The interval is explicit, and no additional side effects or requirements exist. It is complete for the tool's complexity.

    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 zero parameters, so the input schema has no descriptive gaps. The rubric establishes a baseline of 4 for zero-parameter tools, and the description adds no needed semantics beyond what is obvious.

    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 uses a specific verb ('Return') and a clear resource ('a random float') with an exact range ('[0,1)'). It fully distinguishes the tool's purpose from the sibling tool 'wait', leaving no ambiguity.

    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 description does not explicitly state when to use this tool or mention alternatives. However, the usage is implied by the purpose: use it when a random float in [0,1) is needed. No formal guidance is provided.

    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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