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saketh12e

GrabInsurance MCP

by saketh12e
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one generates a premium quote for a specific product, the other classifies deal intent and suggests products. No overlap.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'get_insurance_quote' and 'classify_deal_intent'. The naming is clear and predictable.

    Tool Count3/5

    With only two tools, the server feels thin for a domain that likely requires more operations (e.g., listing products, purchasing). It is on the low end of borderline.

    Completeness2/5

    The tool surface is missing essential operations like listing available products, purchasing or confirming a policy, and managing user profiles. This leaves significant gaps for typical insurance workflows.

  • Average 4.4/5 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
    • 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 MIT 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

  • 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 disclosure. It reveals a key behavioral trait: uses rule-based classification first, then falls back to Claude API if the category is unknown. This gives the agent insight into the tool's decision process. However, it does not disclose potential side effects, authorization needs, or error handling, which would elevate the score further.

    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 concise and well-structured: a one-line summary, a brief overview of the algorithm, and a clear Args list. Every sentence serves a purpose, and the format is easy to parse quickly.

    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 tool with 5 parameters, no output schema, and moderate complexity (classification with fallback), the description provides a thorough overview of inputs and high-level output structure (ClassificationResult fields). It covers the essential mechanics but could improve by detailing edge cases or confidence score semantics. Still, it is largely complete for an agent to use correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Given 0% schema description coverage, the description compensates fully by providing explicit explanations for all 5 parameters in the Args section. It includes example values for merchant, enumerates valid category options, defines subcategory examples, specifies deal value in INR, and describes the optional user_history structure. This adds significant meaning beyond the schema's type and title.

    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 verb 'classify' and resource 'deal intent', and specifies it returns top insurance products. It also outlines the classification approach (rule-based with fallback to Claude API), making the tool's purpose unambiguous and distinct from the sibling tool 'get_insurance_quote' which likely retrieves quotes rather than classifying intent.

    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 usage when a deal object is available and classification of intent into insurance products is needed, but it does not explicitly state when to use this tool versus alternatives like 'get_insurance_quote'. No direct guidance on when not to use or prerequisites is provided, leaving room for ambiguity.

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

  • Behavior5/5

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

    Despite no annotations, the description fully discloses key behaviors: premium floor (Rs 19) and cap (Rs 499), calculation basis (base rate, deal value, risk tier), and default risk tier. No side effects are mentioned, but the tool appears read-only.

    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 and well-structured with a clear first sentence, followed by bullet-like details. The Args/Returns format adds clarity, though it could be slightly more streamlined.

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

    Completeness5/5

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

    Given 3 parameters, no output schema, and no annotations, the description is remarkably complete. It specifies return fields (premium_inr, coverage_inr, validity_days, policy_type), calculation logic, and constraints. No gaps for an agent to interpret.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, but the description adds full semantic meaning: product_id examples, deal_value as INR amount, risk_tier with possible values. This compensates completely for the missing schema descriptions.

    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 'Calculate premium quote for an insurance product' with specific verb and resource. It provides examples of product IDs, making the purpose unambiguous. The sibling tool classify_deal_intent is distinctly different, so no confusion.

    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 explains what the tool does and its inputs, but lacks explicit guidance on when to use it vs. alternatives or when not to use it. There is no mention of prerequisites or context, though the purpose is clear enough for an agent to infer.

    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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