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saivarun1410

insurance-mcp-poc

by saivarun1410
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct aspect of insurance operations: policy document search, application status tracking, and product rule evaluation. There is no overlap in their purposes or outputs.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case: search_policy_documents, get_application_status, lookup_product_rules. The verbs (search, get, lookup) are semantically appropriate and uniform.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to a specific insurance POC use case. Each tool addresses a core need without redundancy, making the count appropriate.

    Completeness4/5

    The main workflows of querying policy content, checking application status, and verifying product rules are covered. Minor gaps exist (e.g., no tools for creating/updating applications), but for a POC the surface is reasonably complete.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 12 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.

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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 burden. It clearly states the returned data: status, pending step, underwriter, and event timeline, implying a read-only query. It does not add explicit permission or side-effect notes, but the 'Look up' phrasing adequately conveys a non-mutating operation.

    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?

    A single sentence that is front-loaded with the verb and resource, then lists the exact information returned. No wasted words or irrelevant details.

    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?

    For a simple query tool with one parameter and no output schema, the description is complete: it specifies the input and enumerates the exact output fields (status, step, underwriter, timeline). No additional context is needed for an agent to invoke it correctly.

    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%; the parameter application_number is well-described with a format example. The description reinforces that lookup is by number but adds little beyond the schema. Baseline 3 is appropriate.

    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 'Look up' with a clear resource 'life insurance application' and a defining attribute 'by its number'. It clearly distinguishes from siblings like search_policy_documents and lookup_product_rules, which focus on different resources and actions.

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

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The context is clear: use this tool when you have an application number and need status information. While it doesn't explicitly mention alternatives, the sibling tools are obviously different, and the usage context is unambiguous. Exclusions are not necessary here.

    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, the description carries the full burden. It discloses that this is a semantic search operation and states the return type: 'Returns ranked excerpts with document ids.' This is sufficient for a read-only search tool, though it omits details like pagination or result 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 three sentences, front-loaded with the core purpose, and every sentence adds value. It is compact, clear, and free of redundancy.

    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?

    For a search tool with a well-described schema, the description covers the purpose, the use case, and the return value. The lack of an output schema is mitigated by explicitly mentioning that results are ranked excerpts with document ids.

    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?

    The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds little parameter-specific meaning beyond reinforcing that the query is natural-language based, which the schema already states.

    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 'Semantic search over policy contracts, riders, underwriting guidelines and disclosure documents', identifying both the verb and resource. It also distinguishes itself from siblings like lookup_product_rules and get_application_status by specifying that this tool answers questions about policy content.

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

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage context: 'Use this to answer questions about what a policy says.' It does not explicitly mention when not to use it or name alternative tools, but the sibling names and the stated purpose make the intended use clear.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that supplying applicant_age, face_amount, or state triggers hard eligibility evaluation and rule flagging. This is valuable behavioral context beyond the schema. However, it does not explicitly state the read-only nature, though 'lookup' implies it.

    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 two sentences, front-loaded with the primary function, and contains no filler. Every word contributes to understanding the tool's behavior.

    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?

    The description covers the core purpose and conditional behavior effectively. Given no output schema, it could have described the return format more explicitly, but for a lookup tool with moderate complexity, it is adequate. The absence of side effects or prerequisites is not a significant gap.

    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?

    Schema coverage is 50%, with descriptions for state and product_code. The description adds meaning by explaining that three optional parameters trigger eligibility evaluation, which is not evident from the schema alone. It stops short of detailing units or semantic constraints for applicant_age and face_amount, but the param names are self-explanatory.

    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 returns a product's issue limits and underwriting rules, which is a specific verb+resource. It also distinguishes itself from siblings by focusing on product rules rather than policy documents or application status.

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

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies when to use the tool: when product rules or eligibility information is needed. It does not explicitly mention alternatives, but sibling names are sufficiently distinct to avoid confusion. Clear context without explicit exclusions.

    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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