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openbnb-org

mcp-server-airbnb

by openbnb-org
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for listings, the other gets details for a specific listing. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow the same 'airbnb_<action>' pattern using snake_case, making them predictable and consistent.

    Tool Count3/5

    With only 2 tools, the server is minimal. While it covers essential search and detail retrieval, it may feel thin for a comprehensive Airbnb integration, but it is borderline acceptable.

    Completeness2/5

    Major functionality is missing: no booking, no user reviews, no listing management. The tool surface is very limited for the Airbnb domain.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • 2 of 15 community issues answered or closed in the last 6 months
    • 4 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • Tools from this server were used 4 times in the last 30 days.

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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?

    With no annotations, the description carries the full burden. It states it gets details and provides links, but does not disclose any behavioral traits such as whether it is read-only, if it makes external requests, error handling, or rate limits. It lacks important context for an AI agent to assess risks.

    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?

    Two sentences, no unnecessary words. Could be slightly more structured (e.g., listing expected outputs), but it is concise and front-loaded with the purpose.

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

    Completeness2/5

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

    Given 8 parameters, no output schema, and no sibling differentiation in description, the description is incomplete. It does not explain what 'detailed information' includes, how errors are handled, or usage considerations like API limits or data freshness. This leaves gaps for the agent.

    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%, so baseline is 3. The description adds no additional meaning beyond what the parameter descriptions in the schema provide. It does not explain how parameters like check-in/out or guests affect the returned data.

    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 'Get detailed information' and the resource 'specific Airbnb listing', and adds 'Provide direct links', which is a distinct output. It differentiates from the sibling 'airbnb_search' tool by implying it returns details for one listing.

    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 using this tool when you have a specific listing ID and want details/links, but it does not explicitly state when to avoid it or mention the sibling tool as an alternative for searching. No exclusion criteria are given.

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

  • Behavior2/5

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

    No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions providing direct links but does not discuss rate limits, data volume, error handling, or the implications of the ignoreRobotsText parameter.

    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?

    Two sentences, no wasted words, front-loaded with the main action. Very concise and structured.

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

    Completeness2/5

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

    With 13 parameters and no output schema, the description is too brief. It does not explain pagination details, return format, response structure, or limits. Lacks completeness for effective use.

    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 has 100% description coverage, so the schema already explains parameters. The description adds generic phrasing 'various filters' and 'pagination' but does not clarify usage beyond schema entries.

    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 searches for Airbnb listings with filters and pagination, and provides direct links. It distinguishes from the sibling 'airbnb_listing_details' which is for details of a specific listing.

    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?

    No explicit guidance on when to use this tool versus alternatives, nor when not to use it. The description simply states the action without context for decision-making.

    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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Glama performs regular codebase and documentation scans to:

  • 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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