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ilgizar-valiullin

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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.10.16

  • Disambiguation5/5

    Each tool serves a distinct purpose: web_search for general web queries, github_search for GitHub-specific searches, status for diagnostics, and report_search_usage for reporting usage. No overlap between them.

    Naming Consistency2/5

    Naming patterns are inconsistent: web_search and github_search are noun_verb, status is a single noun, and report_search_usage is verb_noun. Mixing these styles creates a lack of predictability.

    Tool Count4/5

    4 tools is slightly on the low side for a search server, but the scope is limited to web search, GitHub search, diagnostics, and usage reporting, which feels reasonably complete for the intended purpose.

    Completeness4/5

    The server covers core search needs (web and GitHub) plus operational tools (status and reporting). Missing niche search types like image or news search, but these are not essential for a general-purpose search MCP server.

  • Average 3.8/5 across 4 of 4 tools scored.

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

    • 6 of 10 community issues answered or closed in the last 6 months
    • 78 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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 are provided, so the description bears full responsibility for disclosing behavioral traits. It fails to mention any limitations, rate limits, pagination behavior, authentication requirements, or the nature of the return value. While the read-only nature of a search is implied, the description does not explicitly state it or provide any cautionary 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?

    Two sentences, zero fluff. The description is front-loaded with the core verb and resource, then adds a concise usage preference. Every word earns its place, making it highly efficient.

    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 5 parameters, no output schema, and no annotations, the description is too sparse. It does not explain how to construct a query, what the return format looks like, or how pagination works. The high-level purpose is clear, but operational details are entirely missing, leaving the agent unable to use the tool correctly beyond a simple repository search.

    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, the description must compensate for explaining parameters. It explains the 'type' parameter by listing the searchable entities (repositories, code, issues, users), but it does not clarify the meaning of 'query', 'page', 'stars', or 'language', leaving the agent without guidance on how to use these parameters effectively.

    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 verb ('Search') and resource ('GitHub'), and enumerates the specific searchable entities (repositories, code, issues, users). It also provides a distinctive use case ('open source repos, code patterns, and real-world usage examples'), which differentiates it from sibling tools like web_search.

    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 phrase 'Prefer for open source repos, code patterns, and real-world usage examples' explicitly indicates when to use this tool. It gives clear context but does not mention when not to use it or explicitly name alternatives, though the sibling web_search is implied as the alternative for non-GitHub searches.

    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?

    With no annotations, the description carries the full burden of behavioral disclosure. It states the type of data returned ('diagnostics, provider health, budget state'), implying a read-only operation, but it does not disclose side effects, response format, or any potential caveats such as staleness or access restrictions.

    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, front-loaded sentence that immediately states the verb and resources. Every word is informative, with no redundancy or filler.

    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 that the tool has no input parameters and no output schema, the description covers the essential context: it names the categories of data returned. It is slightly light on what 'server diagnostics' entails or how budget state might be represented, but for a simple status tool it is sufficiently complete.

    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 zero parameters, so there is no ambiguity to resolve. The input schema is empty and description coverage is 100% (vacuously). The baseline for zero parameters is 4, and the description appropriately adds no parameter information since none exist.

    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 retrieves server diagnostics, provider health, and budget state. The verb 'Get' plus the specific resources make the purpose unambiguous and easily distinguishable from sibling tools like web_search and github_search.

    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 explains what the tool does but gives no guidance on when to use it or how it compares to alternatives. It does not mention prerequisites, exclusions, or recommend this tool over others for specific scenarios.

    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?

    There are no annotations, so the description must convey behavioral traits. It conveys that the tool returns current/realtime information and implies a read-only operation, but it does not disclose output format, pagination, rate limits, or any side effects. The reference to 'server instructions' offloads detail rather than providing it.

    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 two sentences, front-loaded with the core purpose, and every word earns its place. The imperative instruction adds practical value without unnecessary verbosity.

    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 simple single-parameter schema and no output schema, the description covers the tool's purpose, topical scope, and a clear usage rule. It could be more complete by stating return value expectations, but the combination of the schema and the pointer to server instructions makes it adequate for invocation.

    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 coverage is 100% and the query parameter description is very detailed (keyword style, quotes, site: operator, date handling). The tool description adds general guidance on when to search but no additional parameter-level detail, so a baseline of 3 is appropriate.

    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 identifies the tool's action ('Search the web') and its scope ('real-time data, current events, documentation, and up-to-date factual information'). It distinguishes itself from the sibling 'github_search' by targeting general web content rather than code repositories.

    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?

    It gives a strong usage directive: always search first for technical specifics rather than relying on training data. It also lists the types of information this tool is meant for, though it does not explicitly mention when to prefer alternative tools like 'github_search'.

    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 are provided, so the description carries the full burden. It explains the purpose and timing but does not disclose any side effects, error conditions, or what happens if called prematurely. For a simple reporting tool, this is adequate but not rich.

    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?

    Two sentences, front-loaded with the purpose and a clear usage instruction. Every word 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 2-param tool with no output schema, the description provides the essential 'what' and 'when.' It could mention what the response looks like or that this is the final step, but the given context is largely sufficient.

    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 params are fully documented there. The description adds no extra meaning beyond what the schema already states, so baseline 3 is appropriate.

    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 states a specific action ('Report which search results were actually cited') and clearly distinguishes this from sibling tools like web_search and status. It also gives a precise context ('in the agent's final answer').

    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 explicitly says 'Call this after you have formulated your response using web_search results,' providing a clear timing condition. It doesn't explicitly name alternatives, but the relationship to web_search is evident and the instruction is unambiguous.

    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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