Skip to main content
Glama

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: ask-rovodev is for AI-powered querying with file analysis, Help provides CLI documentation, and ping is for connection testing. There is no overlap in functionality, making tool selection unambiguous.

    Naming Consistency2/5

    Naming is inconsistent: ask-rovodev uses kebab-case with a verb-noun structure, Help uses PascalCase (or title case) with a noun only, and ping uses lowercase with a verb only. There is no predictable pattern across the tool set.

    Tool Count3/5

    With only 3 tools, the set feels thin for a server named 'Rovodev MCP Tool', which suggests broader capabilities. However, the tools cover basic AI querying, help, and connectivity, so it's borderline but not severely mismatched.

    Completeness2/5

    Inferred domain is AI-assisted development or code analysis, but the tool surface is significantly incomplete. There are no tools for operations like code generation, debugging, version control integration, or configuration management, which are typical in such domains, leading to potential agent failures.

  • Average 3.1/5 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
  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions capabilities like file analysis and large context windows, it lacks critical behavioral details: whether this is a read-only or mutating operation, authentication requirements, rate limits, response format, or error handling. The description covers what the tool can do but not how it behaves operationally.

    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 appropriately concise - a single sentence that efficiently lists key capabilities. It's front-loaded with the core purpose and doesn't waste words. However, it could be slightly more structured by separating distinct capability categories more clearly.

    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?

    For a complex tool with 11 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what kind of response to expect, error conditions, operational constraints, or how the various parameters interact. The description covers 'what' but not 'how' or 'what happens next,' leaving significant gaps for a tool of this complexity.

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

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions '@file or #file syntax' which relates to the 'prompt' parameter, but doesn't provide additional context about parameter interactions or advanced usage patterns. Baseline 3 is appropriate when schema does the heavy lifting.

    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: 'Query Rovodev AI with support for file analysis, codebase exploration, and large context windows.' It specifies the action (query), target (Rovodev AI), and key capabilities. However, it doesn't explicitly differentiate from sibling tools like 'Help' or 'ping' beyond the AI query focus.

    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 mentions general capabilities but provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose this over sibling tools like 'Help' or 'ping', nor any context about appropriate use cases versus other query methods. Usage is implied through feature listing rather than explicit guidance.

    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 are provided, so the description carries the full burden. It states the tool displays help information, implying a read-only, informational operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, or what format the help is in (e.g., text, markdown). This leaves gaps in understanding how the tool behaves beyond its basic purpose.

    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, clear sentence: 'Display rovodev CLI help information.' It is front-loaded with the core action and resource, with zero wasted words. Every part of the sentence earns its place by specifying what is displayed and in what context.

    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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It states what the tool does but lacks details on behavioral aspects like output format or usage context. For a help tool, this might suffice minimally, but more completeness could improve agent understanding, especially without annotations.

    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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately avoids mentioning any. A baseline of 4 is applied since no parameters exist, and the description doesn't introduce confusion by referencing non-existent inputs.

    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: 'Display rovodev CLI help information' specifies the action (display) and resource (help information) with context (CLI). It doesn't explicitly distinguish from sibling tools like 'ask-rovodev' or 'ping', but the purpose is unambiguous for a help function.

    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. It doesn't mention scenarios like needing CLI documentation, troubleshooting, or how it differs from 'ask-rovodev' (which might provide interactive help). Without such context, users must infer usage based on the tool name alone.

    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 are provided, so the description carries the full burden. It states the tool 'echoes a message', which implies a read-only or simple response behavior, but it doesn't disclose details like whether it requires authentication, has rate limits, or what the exact output format is. For a tool with no annotation coverage, this is a significant gap in behavioral context.

    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 directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.

    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 the tool's low complexity (one optional parameter) and high schema coverage, the description is adequate but has clear gaps. It lacks output schema information and doesn't fully compensate for the absence of annotations, leaving behavioral aspects like response format or error handling unspecified.

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

    Parameters3/5

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

    The schema description coverage is 100%, so the schema already documents the single parameter 'prompt' with its type, default value, and description. The tool description doesn't add any additional meaning beyond what the schema provides, such as usage examples or constraints, which aligns with the baseline score when schema does the heavy lifting.

    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 ('echo') and resource ('a message'), explaining it's for testing connections. However, it doesn't explicitly differentiate from sibling tools like 'ask-rovodev' or 'Help', which might also involve communication or testing functions.

    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 implies usage for testing connections, which provides some context, but it doesn't specify when to use this tool versus alternatives like 'ask-rovodev' or 'Help'. No explicit when-not-to-use guidance or prerequisites are mentioned.

    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

rovodev-mcp-tool MCP server

Copy to your README.md:

Score Badge

rovodev-mcp-tool 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/Jaggerxtrm/rovodev-mcp-tool'

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