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SiddarthaKoppaka

Car Deals Search MCP Server

by SiddarthaKoppaka
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching for car deals, making it impossible for an agent to misselect among non-existent alternatives.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'search_car_deals' follows a clear verb_noun pattern, and there are no other tools to create inconsistency or mixed conventions.

    Tool Count2/5

    A single tool is too few for a server named 'Car Deals Search MCP Server', which implies a broader domain of car deal operations. While search is a core function, the lack of tools for filtering, sorting, or managing deals (e.g., save, compare) makes the set feel thin and incomplete for the apparent scope.

    Completeness2/5

    The tool set is severely incomplete for the domain of car deals search. It only provides a basic search function, with obvious gaps such as no tools for refining searches (e.g., by price, mileage), viewing deal details beyond listings, or interacting with deals (e.g., saving favorites). This will likely cause agent failures when more complex tasks are required.

  • Average 3.5/5 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 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

  • 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. It mentions the sources searched and return data, but fails to disclose critical behavioral traits like rate limits, authentication needs, pagination, error handling, or whether it's a read-only operation. For a search tool with 12 parameters, this leaves significant gaps in understanding its behavior.

    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, well-structured sentence that efficiently conveys purpose, sources, and return format without any wasted words. It's front-loaded with the core action and appropriately sized for the tool's complexity.

    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?

    Given the complexity (12 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers purpose and return data at a high level, but lacks details on behavioral aspects, error cases, or output structure. Without annotations or output schema, more context would be beneficial for a tool of this scope.

    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 description coverage is 100%, so the schema fully documents all 12 parameters. The description adds no additional parameter semantics beyond implying filtering capabilities through the return data mentioned. It meets the baseline of 3 since the schema handles the heavy lifting, but doesn't compensate with extra context.

    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 action ('Search for car deals'), resources ('across multiple sources'), and specific sources (Cars.com, Autotrader, KBB). It distinguishes what the tool does with precision, mentioning the return format (listings with prices, mileage, deal ratings, and links). With no sibling tools, this level of specificity is excellent.

    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 for searching car deals across specific sources but provides no explicit guidance on when to use this tool versus alternatives (none exist here), prerequisites, or exclusions. It lacks context about ideal scenarios or limitations, leaving usage to inference from the purpose.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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