Skip to main content
Glama
nghizas
by nghizas

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.1

  • Disambiguation2/5

    The tools `check_downloads` and `check_package_version` are both specific to package registries, and `lookup_fact` can replicate their functionality by querying the same sources (npm/pypi) with appropriate fields, causing ambiguity. `check_service_status` is distinct but also could be covered by `lookup_fact` with source 'statuspage'.

    Naming Consistency4/5

    Three tools use the `check_` prefix, while `lookup_fact` uses a different verb but still follows a verb_noun pattern. The naming is mostly consistent and predictable.

    Tool Count4/5

    With 4 tools, the set is small but reasonable for checking package metrics and service status. The redundancy suggests a slightly lower count could suffice, but overall it is not excessive or insufficient.

    Completeness3/5

    The server covers basic checks for npm/PyPI packages and cloud services, but lacks operations like listing available packages/services, comparing versions, or retrieving more detailed metadata beyond the predefined fields. Some intended functionality is indirectly available via `lookup_fact`, but the surface feels gappy.

  • Average 3.8/5 across 4 of 4 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?

    No annotations provided, so description carries full burden. It only says 'look up', implying a read operation, but does not disclose behavioral traits like idempotency, rate limits, or authentication requirements. Lacks transparency for a tool with no 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?

    Very concise, with a clear main sentence followed by structured argument list. No unnecessary words. Information is front-loaded.

    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 a simple lookup with 3 parameters and an output schema (though not detailed), the description is mostly complete. It covers the inputs well, but lacks any example output or confirmation of the return format. Given no annotations, it could be slightly more detailed.

    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?

    Schema coverage is 0%, but the description provides examples and enumerates possible values for each parameter (e.g., source: 'npm', 'pypi', 'statuspage'; field: 'latest_version', 'license', etc.), adding significant meaning beyond the bare schema titles. However, it does not cover all parameters exhaustively.

    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 states it looks up facts tracked by Grounded, which is a specific verb-resource combination. However, it does not differentiate from siblings like check_downloads or check_package_version, which are more specialized. The purpose is clear but could be more explicit about scope.

    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?

    No guidance on when to use this tool versus alternatives. There is no mention of when-not to use it or any context for selection among siblings. The description only explains parameters.

    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 must carry the full burden. It only states the action without disclosing behavior like data source, rate limits, or error handling. This leaves transparency gaps.

    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 extremely concise: a single sentence followed by a brief docstring. It front-loads the primary action and avoids any redundancy.

    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, the description covers the core inputs. However, it does not mention output format or potential errors, though an output schema exists. It is adequate but not thorough.

    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?

    With 0% schema description coverage, the description compensates by providing examples for package_name and enumerating registry values ('npm' or 'pypi'). This adds meaningful context beyond the 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 it checks the latest version of a package on npm or PyPI, specifying the verb 'check' and the resource 'latest version of a package'. It distinguishes from siblings like check_downloads (download counts) and check_service_status (service health).

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

    Usage Guidelines4/5

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

    The description implies usage context (checking package versions on npm/PyPI) but does not explicitly state when not to use or provide alternatives. The sibling tools are distinct, so no confusion arises.

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

  • Behavior3/5

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

    No annotations provided, so description carries burden. It states it checks weekly download count, but does not disclose rate limits, caching behavior, or error handling. Adequate for a simple read operation but leaves some traits implicit.

    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?

    Description is concise with no fluff. Purpose stated upfront, parameter explanations follow immediately. Each sentence earns its place.

    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 a simple tool with few parameters and an output schema, description covers purpose and parameters well. Could mention that it returns weekly data, but output schema likely clarifies. Good enough.

    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?

    Schema description coverage is 0%, so description must compensate. It adds meaning with examples for package_name and clarifies registry values and default. Lacks validation details but adds significant value beyond 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 it checks weekly download count for a package on npm or PyPI, with specific verb and resource. It distinguishes from siblings like check_package_version (checks version) or check_service_status (checks service status).

    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?

    No explicit when-to-use or when-not-to-use guidance, but the examples and registry options imply usage for download statistics. Could be improved by mentioning alternatives like check_package_version for version info.

    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 full burden. It mentions 'via its status page' but lacks details about the mechanism (e.g., HTTP request, possible delays, caching) or what the returned status looks like. For a check tool, more transparency about output format or potential errors would be beneficial.

    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?

    Extremely concise: two sentences with purpose and parameter description. No extraneous words; front-loaded with the action. Every sentence earns its place.

    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 simplicity (one parameter, clear purpose) and the presence of an output schema, the description is largely complete. It could optionally describe the return type, but the output schema likely covers that. The description adequately supports an agent in using the tool.

    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?

    Only one parameter (service_name), and schema coverage is 0%. The description adds value by listing example service names, which clarifies valid inputs beyond the schema's type string. However, it does not specify format requirements (e.g., case sensitivity or exact naming).

    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?

    Clearly states the tool's purpose: checking the current status of a cloud service via its status page. The verb 'Check' and resource 'status of a cloud service' are specific. It is distinct from sibling tools that deal with downloads, package versions, or facts.

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

    Usage Guidelines4/5

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

    Provides concrete examples of service names (e.g., 'github', 'stripe'). Implies when to use: when you need to verify service health. Does not explicitly state when not to use or contrast with alternatives, but the clear purpose and examples offer sufficient guidance for this simple tool.

    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

grounded-mcp MCP server

Copy to your README.md:

Score Badge

grounded-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/nghizas/grounded-mcp'

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