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BACH-AI-Tools

Realty In Ca1 MCP Server

by BACH-AI-Tools
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Python
Remote

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation4/5

    Most tools have distinct purposes targeting different resources (agents, keywords, locations, properties), but there is some overlap between 'locationsauto_complete' and 'locationsv2auto_complete' which could cause confusion about which to use. The property-related tools are well-differentiated by their specific functions.

    Naming Consistency3/5

    The naming follows a general pattern of resource_action (e.g., agentslist, propertiesdetail), but there are inconsistencies like 'propertiesget_demographics' (missing underscore), 'propertiesget_statistics_deprecated' (mixed conventions), and the v2 suffix in 'locationsv2auto_complete'. This mixed approach reduces predictability.

    Tool Count5/5

    With 12 tools, this is well-scoped for a real estate server covering agents, locations, and properties. Each tool appears to serve a specific purpose without obvious bloat, and the count supports comprehensive functionality without being overwhelming.

    Completeness4/5

    The toolset provides good coverage for real estate queries, including listing, details, filtering, and location-based data. Minor gaps exist, such as no explicit tools for creating or updating listings (likely read-only), but agents can work around this for typical search and analysis tasks.

  • Average 2.7/5 across 12 of 12 tools scored. Lowest: 2.1/5.

    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 is passing

  • This repository is licensed under MIT License.

  • 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.

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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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