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aindreyway

MCP Server Neurolorap

by aindreyway
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: code_collector focuses on extracting code content into a markdown document, while project_structure_reporter generates metrics about the project's structure. There is no overlap in functionality, making it easy for an agent to choose the right tool.

    Naming Consistency5/5

    Both tools follow a consistent noun_verb pattern (code_collector and project_structure_reporter), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for a domain like project analysis or code management. This minimal set may not cover essential operations such as code analysis, dependency checking, or file manipulation, limiting its utility for broader tasks.

    Completeness2/5

    Inferred domain is project/code analysis, but the tool surface is severely incomplete. It lacks basic CRUD operations (e.g., no tools for creating, updating, or deleting files), code quality checks, or integration with version control, leaving significant gaps that could cause agent failures.

  • Average 2.9/5 across 2 of 2 tools scored.

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

  • 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"
  ]
}

    Then . Browse examples.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action. It does not cover critical aspects like whether this is a read-only operation, if it modifies files, error handling, performance implications, or output details. The description is insufficient for a tool with 3 parameters and an output schema.

    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 zero wasted words. It is front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place, though this conciseness comes at the cost of completeness.

    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 3 parameters with 0% schema coverage, an output schema, and no annotations, the description is inadequate. It does not explain parameter roles, behavioral traits, or how the output schema relates to the markdown document. The presence of an output schema reduces the need to describe return values, but other gaps remain significant.

    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%, so the description must compensate but adds no parameter information. It does not explain what 'input_path', 'title', or 'subproject_id' mean, their formats, or how they affect the collection process. The description fails to provide any semantic context beyond the tool's name.

    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 ('collect') and resource ('code from files'), specifying the output format ('into a markdown document'). It distinguishes from the sibling 'project_structure_reporter' by focusing on code content rather than structure, though the distinction could be more explicit.

    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 the sibling 'project_structure_reporter'. It lacks context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the purpose statement.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate a report' implies a read-only operation that creates output, it doesn't specify whether this tool scans files, requires specific permissions, has performance implications for large projects, or what format the report takes. The description lacks important behavioral context for a tool that presumably analyzes project structure.

    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 gets straight to the point with no wasted words. It's appropriately sized for what it communicates, though what it communicates is minimal. The structure is clear and front-loaded with the core 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 that there's an output schema (which should document the return format), the description doesn't need to explain return values. However, for a tool that analyzes project structure with 2 parameters and no annotations, the description is too minimal. It doesn't provide enough context about what 'project structure metrics' includes, how the tool works, or what the parameters control.

    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?

    With 0% schema description coverage for both parameters, the description provides no information about what 'output_filename' or 'ignore_patterns' mean or how they should be used. The description doesn't mention parameters at all, leaving the agent to guess their purpose from parameter names alone. This is inadequate for a tool with 2 parameters that have no schema documentation.

    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 'Generate a report of project structure metrics' clearly states the verb ('Generate') and resource ('report of project structure metrics'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'code_collector' - both could potentially involve project analysis, so the distinction isn't explicit.

    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 this tool is appropriate, what prerequisites might be needed, or how it differs from the sibling 'code_collector' tool. The agent must infer usage context entirely from the tool name and description.

    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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  • Evaluate tool definition quality.

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