Skip to main content
Glama
mnemox-ai
by mnemox-ai

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.5.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clearly defined purpose: checking if a product idea already exists. No other tools exist that could be confused with it.

    Naming Consistency5/5

    The tool name 'idea_check' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect as there are no other names to compare against or deviate from any pattern.

    Tool Count2/5

    A single tool is too few for a server named 'idea-reality-mcp', which suggests a broader scope related to idea validation or market research. While the tool is well-described, the server feels thin and incomplete with just one operation, limiting its utility for agents handling complex idea evaluation workflows.

    Completeness2/5

    The server is severely incomplete for its implied domain of idea reality checking. It only offers a check operation but lacks tools for related tasks like analyzing market trends, comparing features, tracking idea evolution, or managing a portfolio of ideas. This creates significant gaps that will hinder agents from performing comprehensive idea assessment.

  • Average 4.8/5 across 1 of 1 tools scored.

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

    • 1 of 2 community issues answered or closed in the last 6 months
    • 35 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing
  • 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.

  • This repository includes a glama.json configuration file.

  • 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

  • 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 behavioral disclosure. It effectively describes what the tool does (checks for existing ideas), mentions sources (GitHub + HN for 'quick', all sources for 'deep'), and outlines the return format (reality check report with specific components). It doesn't mention rate limits, authentication needs, or error handling, but covers the core behavior well for a tool without annotations.

    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 well-structured and front-loaded with the core purpose. Each sentence earns its place: the first states what it does, the second provides usage guidelines, the third lists trigger phrases, and the last sections explain parameters and returns. There's no wasted text, and it's appropriately sized for the tool's complexity.

    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?

    Given the tool has 2 parameters (1 required), 0% schema description coverage, no annotations, but has an output schema, the description provides excellent contextual completeness. It explains the tool's purpose, when to use it, parameters, and return format. The output schema existence means the description doesn't need to detail return values, and it appropriately focuses on semantics and usage.

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

    Parameters5/5

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

    The schema description coverage is 0%, so the description must compensate fully. It provides clear semantic explanations for both parameters: 'idea_text' is described as 'Natural-language description of the idea' and 'depth' is explained with its two enum values ('quick' uses GitHub+HN, fast; 'deep' uses all sources in parallel). This adds significant value beyond the bare schema.

    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 tool's purpose with specific verbs ('Check if a product idea already exists') and resource ('product idea'). It distinguishes the tool's function from potential alternatives by specifying it's for validation before building. The title is null, so the description carries the full burden and does so effectively.

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

    Usage Guidelines5/5

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

    The description provides explicit guidance on when to use the tool: 'when users discuss new project ideas, ask about competition, market saturation, or whether something has been built before.' It includes specific trigger phrases in multiple languages, making it very clear about the appropriate context for invocation. No sibling tools exist, so differentiation isn't needed.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

idea-reality-mcp MCP server

Copy to your README.md:

Score Badge

idea-reality-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mnemox-ai/idea-reality-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server