Skip to main content
Glama
mafedelahoz

CuddlyNest Search & Listings MCP Server

by mafedelahoz
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one resolves destinations via autosuggestion, while the other retrieves details for a specific known hotel. There is no meaningful overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools share the cuddlynest_ prefix and use snake_case, but one uses a verb phrase (search) while the other uses a noun phrase (listing_details). The pattern is readable and predictable, with only minor stylistic inconsistency.

    Tool Count3/5

    With only two tools, the server feels thin for a 'Search & Listings' offering. The tools cover two discrete actions, but the count is borderline and likely limits what agents can accomplish.

    Completeness2/5

    The server can resolve a destination and fetch details for a known hotel, but it lacks any tool to enumerate or search hotels within a destination. This creates a significant workflow gap: an agent cannot go from destination selection to a list of available properties.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full behavioral disclosure burden. It does well by revealing the two data sources — a listing page and a wholesale-supplier WebSocket — and by noting that live data is accumulated across partial messages. It does not mention reliability, rate limits, or exact asynchronous behavior of the WebSocket, but the disclosed behavior is materially useful.

    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 a single dense sentence that front-loads the core action and then layers static vs. live data sources, ending with the partial-message accumulation caveat. It is efficient and readable, though the parentheticals make it somewhat long; no sentence is wasted.

    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?

    Given 11 parameters, no annotations, and no output schema, the description covers the essential context: what data is returned, where it comes from, and what the live parameters are for. It does not describe the return shape or partial-failure behavior in detail, but the listed result categories are enough for an agent to invoke the tool correctly.

    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 description adds useful overall context by clarifying that parameters like checkin/checkout/adults drive the live pricing and availability stream, while the hotel parameter identifies the listing. It does not add per-parameter detail beyond what the schema already provides.

    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 and resource ('Get details for a specific CuddlyNest hotel') and lists concrete result categories: static basics, live room options, prices, availability, and cancellation policies. This clearly distinguishes it from the sibling cuddlynest_search, which by name handles search rather than retrieval 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The intended use is clear: call this when you already have a specific CuddlyNest hotel and want both static and live details. However, it does not explicitly name cuddlynest_search or state when not to use this tool, so it falls just short of fully explicit usage guidance.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. It does reveal that this is an autosuggestion/destination-resolution step, not a full hotel-price enumerator, and calls out that a further capture is needed. However, it does not disclose response shape beyond 'candidates' and 'internal slug', nor does it address error behavior, rate limits, or whether the call is read-only. With no annotations, more behavioral context would be expected for a higher score.

    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 with no filler. The core purpose is front-loaded, and the caveat about the need for a further capture is concise and actionable. Every sentence earns its place.

    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?

    The tool has 9 parameters, no output schema, and no annotations, so complexity is moderate-high. The description gives a clear purpose and next-step routing, but it leaves ambiguity about why parameters like checkin, checkout, rooms, adults, and currency are present in an autosuggestion call and what exactly the returned candidates look like beyond the slug. Adequate for basic use, but with clear gaps.

    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 description adds a little meaning around the destination and slug, but it does not clarify how the optional occupancy, date, or currency parameters relate to the autosuggestion step or whether they are pass-through values for later pricing. The schema already documents each parameter, so this is adequate but not additive.

    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 uses a specific verb and resource: it resolves a destination to autosuggestion candidates and surfaces the internal slug for pricing lookups. It also explicitly distinguishes itself from cuddlynest_listing_details by noting it does not enumerate hotels with prices, so an agent can tell the tools apart.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    It clearly states when to use this tool: to resolve a city/area destination to candidates with slugs. It also explains what this tool is not for, enumerating hotels with prices, and directs the agent to cuddlynest_listing_details for live room prices on a known hotel. This is explicit routing with an alternative.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-cuddlynest MCP server

Copy to your README.md:

Score Badge

mcp-cuddlynest MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mafedelahoz/mcp-cuddlynest'

If you have feedback or need assistance with the MCP directory API, please join our Discord server