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Work90210

APIFold

by Work90210
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

75%
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 lists all available servers (discovery), while the other retrieves a specific connection endpoint (retrieval). No functional overlap exists between listing a catalog and fetching a specific URL.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern using snake_case (get_server_endpoint, list_servers). The verb choices ('get' vs 'list') accurately reflect the read operations and maintain predictable conventions.

    Tool Count3/5

    With only 2 tools, the surface feels minimal/thin despite the server connecting to 18 different API proxies. While functional for pure discovery (list + get), the count is borderline for the implied richness of the domain.

    Completeness4/5

    Covers the core read-only lifecycle for a directory service: discovery (list) and connection (get endpoint). Minor gap in filtering/search capabilities for the 18 servers, though listing all is acceptable at this scale.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under AGPL 3.0.

  • 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.

  • This repository includes a glama.json configuration file.

  • 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 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 must carry the full burden of behavioral disclosure. It mentions what is retrieved (SSE endpoint URL) but omits error handling (e.g., invalid slug), side effects, caching behavior, or whether this operation incurs costs or rate limits.

    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 front-loads the action verb. There is no redundant or wasted text; every word contributes to understanding the tool's function.

    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?

    While adequate for a simple two-parameter lookup tool, the description lacks information about the return value format (critical given no output schema exists) and error conditions. It meets minimum viability but leaves operational gaps.

    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?

    With 100% schema description coverage, the schema already comprehensively documents both parameters (including examples for 'slug'). The description adds no additional parameter semantics beyond implying the 'slug' parameter via 'by slug', so it meets the baseline expectation.

    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 uses a specific verb ('Get') and resource ('SSE endpoint URL') and clearly scopes the operation to a specific server identified 'by slug'. This effectively distinguishes it from sibling tool 'list_servers' (which presumably enumerates servers rather than retrieving a specific endpoint).

    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 fails to mention that 'list_servers' should likely be used first to discover valid slugs, or under what circumstances an agent should call this endpoint.

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

  • Behavior4/5

    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. It successfully reveals critical ecosystem constraints: the servers are 'free hosted,' 'proxy a REST API,' and require users to 'bring your own API key.' However, it omits details about response format, caching, or rate limiting.

    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 consists of two highly efficient sentences. The first front-loads the core deliverable (the 18 specific servers), while the second adds essential context about the REST proxy architecture and API key requirements. No words are wasted.

    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 low complexity (zero parameters, no nested objects) and lack of output schema, the description adequately explains what the tool returns (inventory of 18 servers) and their operational model. It could be improved by hinting at the return structure (e.g., array of server objects), but it is sufficiently complete for agent selection.

    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?

    Per the evaluation rules, tools with zero parameters receive a baseline score of 4. The input schema is an empty object with no parameters to describe, and the description appropriately focuses on the return value rather than non-existent inputs.

    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 'List[s] all 18 free hosted APIFold MCP servers' with specific examples (GitHub, Stripe, Slack, etc.). The plural 'servers' and enumeration of all 18 naturally distinguishes it from sibling 'get_server_endpoint' (singular), making the scope unambiguous.

    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 this is a discovery/initialization tool ('List all'), but provides no explicit guidance on when to use this versus 'get_server_endpoint' or prerequisites for using the listed servers. The usage context is inferred from the verb 'List' but not stated explicitly.

    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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