Booking MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Search and place details have clear boundaries: search accepts destination criteria and returns property lists, while place details consumes a single property URL and returns full property information. The overlap in returned pricing/rating fields is expected, not confusing.
Naming Consistency5/5Both tool names follow the same hasdata_booking_<endpoint>_get... pattern, using place and search as distinct resource endpoints. The naming is consistent across the set, even though the operation suffix uses camelCase.
Tool Count3/5Two tools is a minimal but reasonable set for a search-then-detail workflow. However, the count sits at the thin edge of the expected 3-15 tool range, leaving little room for exploration beyond the two core endpoints.
Completeness5/5The tool surface covers the intended read-only Booking.com workflow: search for accommodations, then fetch a single property's full details. There are no dead ends for travel-planning or OTA data monitoring use cases.
Average 4.2/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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 is passing
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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?
No annotations are provided, so the description carries the behavioral burden. It discloses that this is a read-style 'Fetches' operation, that it returns real-time availability and pricing, and it summarizes the outputs including policies, ratings, photos, and room suites. It does not mention limiting behaviors such as rate handling or response failure conditions, but it is substantially transparent for a GET-like lookup.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a well-organized paragraph: what it does, what it returns, and when to use it. The first line 'Get Booking Hotel Details' is slightly redundant with the name, but the rest of the description avoids unnecessary noise and information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description compensates by listing the key return categories and explaining enriched use cases. It could include more detail about exact response structure or error conditions, but for the AI agent the combination of schema, use cases, and return summary is enough to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full descriptions for all 9 parameters, so the schema does most of the parameter work. The description adds context by tying the inputs to stay dates and guest composition, and by mentioning the URL origin flow from search, but it does not deepen the individual parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Fetches a single Booking.com property by its full URL.' It names the key inputs and outputs and distinguishes itself from the sibling by being a detail lookup for an already-known property URL rather than a discovery/search call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives concrete use cases: enrich property listings, monitor a competitor hotel, validate amenities/photos, and fetch details after a search. It does not explicitly state when not to use it versus the search endpoint, but it clearly implies this tool is for known URLs and detailed property data.
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 provided, the description carries the full burden, and it does a good job: it reports that this is an external Booking.com search, that pagination is page-based with 25 results per page, that language and currency control locale, and it enumerates the returned hotel data. It does not cover possible errors, rate limits, or authorization requirements, but for a read-oriented search tool the behavioral disclosure is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long, but given the tool's 28 parameters and rich return payload, nearly every sentence adds useful information. It is front-loaded with the core search behavior and filters before covering output and use cases; only the list of use cases is somewhat optional, but it still helps an agent choose the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides a complete picture for selecting and invoking the tool: required search inputs, available filters, pagination, locale handling, output contents, and the relationship with the Booking Place endpoint. Since there is no output schema, the explicit enumeration of return fields is especially valuable and covers what an agent needs to understand the result shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all 28 parameters with 100% coverage, so the baseline is 3. The description restates filter categories and some behaviors (e.g., guest composition, price range, pagination), but it adds limited semantic value beyond what the schema already provides for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Searches Booking.com for accommodations') and a specific resource (destination keyword, stay dates, guest composition). It also differentiates itself from the sibling tool by noting that the returned `hotelId` / URL can be fed into the Booking Place endpoint for full property details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives strong usage context: it is for travel-planning, price/inventory monitoring, competitor analysis, and lead generation, and it points to the Booking Place endpoint as a downstream step for full property details. However, it does not explicitly state when NOT to use this tool or offer a direct comparison between the search and place endpoints, so the alternative guidance is implied rather than fully explicit.
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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