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satya01-sap

SAP Ariba MCP Server

by satya01-sap

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or misselection between tools. The single tool's purpose is clearly defined.

    Naming Consistency5/5

    The one tool name (get_event_summary) follows a clear verb_noun pattern and is internally consistent. Since there are no other tools, there are no naming inconsistencies.

    Tool Count2/5

    A single tool is far too few for a server named 'SAP Ariba MCP Server,' which implies a broad procurement/sourcing domain. This is a significant under-provisioning for the apparent scope.

    Completeness1/5

    The tool only retrieves supplier bids for a sourcing event. There are no operations for managing events, suppliers, bids, or any other typical lifecycle actions, making the surface severely incomplete for the stated domain.

  • Average 3.2/5 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
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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    With no annotations, the description must shoulder the burden of behavioral disclosure. 'Retrieve' implies a read-only operation, and the credential arguments hint at authentication requirements, but the description does not mention error behavior, side effects, return format, or any special constraints. This is minimal.

    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 compact and well organized: one purpose sentence followed by a tidy args block. Every element serves a function, and the examples are efficiently embedded. No filler or redundant content.

    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?

    For a 4-parameter retrieval tool with an output schema available, the description supplies the call parameters and examples, which is decent. However, it omits when-to-use guidance, where credential values come from, and any operational context such as error conditions or required permissions. It is usable but not fully self-contained.

    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 schema provides only property titles with 0% description coverage, so the description's Args block is essential. It labels each parameter and gives concrete examples (e.g., doc_id as 'Doc1213131', password_adapter as 'myadaptor'), which materially helps an agent understand expected values. It does not explain edge cases or validation, but it compensates well for the bare schema.

    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 a specific operation: 'Retrieve supplier bids for a sourcing event.' The verb and resource are explicit, though there is a slight mismatch between the tool name ('event summary') and the described output ('supplier bids'). No sibling tools exist, so differentiation isn't possible, but the core purpose is understandable.

    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 about when to use this tool, what scenarios it applies to, or when to prefer an alternative. The description is purely declarative and provides no context about typical use cases or prerequisites beyond listing required arguments.

    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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