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HasData

com.hasdata/redfin

Server Quality Checklist

83%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches for listings, the other fetches details for a specific property. An agent would not confuse them because they operate on different resource types and return different data.

    Naming Consistency4/5

    Both tools follow the hasdata_redfin_<resource>_get<Detail> pattern, which is consistent. The first tool's name is slightly redundant ('listing_getRealEstateListings') but still predictable and aligned with the second tool.

    Tool Count3/5

    With only two tools, the server feels minimal but not unreasonable. It covers the two most common Redfin needs (search and property details), yet the surface is thin compared to typical MCP servers that include more resource endpoints.

    Completeness4/5

    The pair covers the essential workflow: search listings, then dive into property details. Missing operations like agent search or market stats are non-critical, and the property details endpoint includes comparables and price history, so there are no obvious dead ends.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • Last stable release on
    • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 must fully disclose behavioral traits. It only says it 'fetches' and returns data, but does not address authentication, rate limits, error behavior, or what happens with invalid URLs. This leaves the agent without operational expectations beyond the basic return field list.

    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 front-loaded with the core action and includes no filler. The list of returned fields and use cases is lengthy but informative; however, it could have been trimmed slightly without losing essential guidance, so it does not earn a perfect score.

    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 simple one-parameter fetch tool with no output schema, the description covers the input, the returned data, and common use cases, which is largely sufficient for correct invocation. It falls short of complete because behavioral caveats such as rate limits and error handling are missing.

    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 single parameter 'url' is fully described in the schema with 100% coverage, so the schema already provides the needed semantics. The description adds no further format, examples, or constraints beyond what the schema states, justifying the baseline score of 3.

    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 action and resource: 'Fetches the full Redfin property page by URL' and lists the many fields it returns. However, it never explicitly contrasts itself with the sibling listing tool, so the distinction is left to inference rather than stated outright.

    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 explicitly lists use cases: 'Use for CMA reports, investor due-diligence, valuation models, listing enrichment, and powering buyer-assistant agents.' This gives clear context for when the tool is appropriate, but it does not mention when not to use it or point to the sibling tool as an alternative.

    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 disclosure burden. It discloses pagination, the accepted keyword forms, the full set of returned listing fields, and the important behavioral exception that an address/building returns a single property card and ignores `type`. Rate-limit and error behavior are absent, but the core behavioral surface is well covered.

    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?

    Four dense, front-loaded sentences with no filler: action and pagination first, then location semantics, return fields/exception, and use cases. It is well sized for a 59-parameter tool because the schema carries the detailed filter documentation.

    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?

    Despite no output schema and no annotations, the description compensates by naming the main returned fields and explaining the single-property-card case. For a large search tool, the use cases and sibling routing make it reasonably complete, though it omits pagination-count/default behavior and error-handling expectations.

    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 baseline is 3. The narrative mostly re-states the `keyword` semantics and the `type` exception that already exist in the schema, and it adds no new information about `page`, `sort`, or the other filter parameters. It therefore earns no bonus beyond the well-documented schema.

    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 opens with a specific, verb-driven statement: 'Searches Redfin for-sale, for-rent, or sold listings with pagination,' and enumerates the location forms and return fields. This makes the listing-search scope unmistakable and differentiates it from the single-property sibling endpoint.

    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?

    It gives concrete use cases ('real-estate market research, lead generation for agents, price/DOM trend analysis') and points to the 'Redfin Property endpoint' for deep-dive details, effectively routing an agent to the sibling after this search step. It does not phrase this as a hard when-not-to-use rule, but the guidance is unambiguous enough.

    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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