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Funmula-Corp

BigGo MCP Server

by Funmula-Corp
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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: one for product search and one for price history. There is no overlap or ambiguity.

    Naming Consistency4/5

    Both tools use snake_case and are descriptive, but 'price_history_with_url' includes a preposition while 'product_search' is simpler, showing slight inconsistency in structure.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a product search domain, limiting its utility. Typically at least 3-5 tools are expected.

    Completeness2/5

    Missing essential tools like product detail, category listing, or filters. The surface only covers search and price history, leaving significant gaps.

  • Average 1.6/5 across 2 of 2 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
    • Last stable release on
    • 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.

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  • 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.

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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

  • Behavior1/5

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

    No annotations are provided, and the description does not disclose any behavioral traits (e.g., request limits, authentication needs, side effects). The description is minimal and adds no transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness1/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two words, which is not an appropriate size. It is under-specification rather than conciseness, as it fails to provide any useful information beyond the tool name.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite having an output schema, the description gives no context about the tool's behavior, return values, or when to use it. It is completely inadequate for a tool with 1 parameter and a sibling tool.

    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% with a single parameter 'query' described as 'Search query' with examples. The description adds no extra meaning beyond the schema, so the baseline score of 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose1/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description is just 'Product Search', which is a tautology of the tool name. It does not specify a verb or resource, and does not distinguish it from the sibling tool 'price_history_with_url'.

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

    Usage Guidelines1/5

    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 or when to avoid it. No alternatives or context are mentioned.

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

  • Behavior1/5

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

    With no annotations, the description must disclose behavioral traits. It does not mention whether the operation is read-only, safe, or destructive, nor does it describe any side effects, authentication needs, or rate limits. The agent gets zero behavioral insight.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely short (5 words) but under-specified, not concise. It lacks structure and fails to provide essential information, making it inadequate for clear communication.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite the tool's simplicity (1 parameter, output schema present), the description is severely incomplete. It does not explain the tool's purpose, behavior, or return value, leaving significant gaps for an agent to infer.

    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 baseline is 3. The description adds no additional meaning beyond the schema's 'Product URL' for the 'url' parameter. It does not specify format, constraints, or examples, but the schema already covers the parameter adequately.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Product Price History With URL' is a noun phrase that merely echoes the tool name without specifying a verb or action. It does not clearly state what the tool does (e.g., retrieve, update, display), leaving the agent uncertain about its 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?

    No guidance is provided on when to use this tool versus the sibling 'product_search'. There is no mention of contexts, alternatives, or when not to use it, making it hard for an agent to choose correctly.

    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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