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DiscreteTom

Mock MCP Server

by DiscreteTom
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'mock_echo' has a single, clear purpose of echoing messages, making it perfectly distinct.

    Naming Consistency5/5

    The tool name 'mock_echo' follows a consistent verb_noun pattern (echo as the verb, implied message as the noun). Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and suggests a trivial or incomplete scope. For a server named 'Mock MCP Server', which implies broader mocking capabilities, one tool feels insufficient and under-scoped.

    Completeness2/5

    The server's purpose is unclear from the name 'Mock MCP Server', but a single echo tool suggests severe incompleteness. It lacks coverage for common mocking operations like simulating data, errors, or varied responses, creating significant gaps that could cause agent failures in typical use cases.

  • Average 3.1/5 across 1 of 1 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.

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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. It states the tool 'echoes back' the message, which implies a read-only or non-destructive operation, but doesn't disclose details like whether it modifies the input, requires authentication, has rate limits, or what the output format is. The description is minimal and lacks behavioral context beyond the basic action.

    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 no wasted words, clearly front-loaded with the core action. It's appropriately sized for a simple tool, making it easy to scan and understand quickly.

    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?

    Given the tool's low complexity (1 parameter, no annotations, but has an output schema), the description is complete enough for basic use. The output schema likely handles return values, so the description doesn't need to explain them. However, it could benefit from more behavioral context, but for this simple case, it's adequate.

    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 input schema has 1 parameter with 0% description coverage, so the schema provides no semantic details. The description adds meaning by specifying that the parameter is a 'message' to be echoed back, which clarifies its purpose beyond the schema's type definition. However, it doesn't detail constraints or examples, but with only 1 parameter, this is sufficient for baseline understanding.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Echo back the provided message' states what the tool does with a clear verb ('echo back') and resource ('message'), but it's somewhat vague as 'echo' could imply various behaviors like returning the exact input or processing it. With no sibling tools, differentiation isn't needed, but the purpose could be more specific about the exact behavior.

    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, as there are no sibling tools mentioned, but it also lacks context on prerequisites or typical use cases. It's a basic tool with implied usage for testing or debugging, but no explicit guidelines are given.

    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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