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Caiuriuller

SRP Hub MCP

by Caiuriuller
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as creating a new customer/lead in the hub, leaving no room for confusion or misselection.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (post_customer), and with no other tools to compare against, there is no inconsistency in naming conventions. The naming is straightforward and predictable.

    Tool Count2/5

    One tool is too few for a server named 'SRP Hub MCP', which implies a broader hub functionality. This minimal toolset feels thin and inadequate for typical hub operations like managing, updating, or retrieving customer data, limiting its usefulness.

    Completeness2/5

    The tool surface is severely incomplete for a hub domain. While it allows creating customers, there are significant gaps: no tools for reading, updating, deleting, or searching customers, which are essential for basic CRUD operations and will likely cause agent failures in handling customer data.

  • Average 2.9/5 across 1 of 1 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 ISC License.

  • This repository includes a README.md file.

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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 carries the full burden of behavioral disclosure. It mentions creating a new customer/lead, implying a write operation, but doesn't cover critical aspects like authentication requirements, error handling, rate limits, or what happens on success/failure. This leaves significant gaps for an agent to understand the tool's behavior.

    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 concise with two sentences that are front-loaded and to the point. The first sentence states the purpose, and the second provides usage context, with no wasted words. However, it could be slightly more structured by explicitly listing key parameters or outcomes.

    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 complexity of a write operation with 5 required parameters, low schema description coverage (40%), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, parameter meanings beyond the schema, or expected outputs, making it inadequate for an agent to use the tool effectively.

    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?

    Schema description coverage is 40%, with only 2 out of 5 parameters having descriptions in the schema. The tool description adds no information about parameters, failing to compensate for the low coverage. It doesn't explain what 'codigo', 'nomeFantasia', or 'status' mean, leaving key inputs undocumented.

    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 action ('Cria um novo cliente') and resource ('no Hub'), specifying it's for creating a new customer/lead. It distinguishes the purpose by mentioning it's for registering leads from other sources, though without sibling tools, differentiation isn't applicable. However, it's slightly vague about what 'Hub' refers to.

    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 provides implied usage guidance by stating 'Use esta ferramenta para registrar leads encontrados em outras fontes,' which suggests when to use it (for leads from other sources). However, it lacks explicit when-not-to-use scenarios or alternatives, and with no sibling tools, broader context is limited.

    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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