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Glama
sind00
by sind00

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
FLIPPA_BASE_URLNoOverride the Flippa API base URLhttps://flippa.com/v3
FLIPPA_API_TOKENNoBearer token for future authenticated endpoints (watchlist, bidding)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
flippa_search_listingsA

Search and browse listings on the Flippa marketplace.

Args:

  • property_type: Filter by type (website, saas, ecommerce_store, fba, ios_app, android_app, ai_apps_and_tools, youtube, newsletter, service, other)

  • status: Filter by status (open, closed, ended). Default: "open"

  • sale_method: Filter by sale method (auction, classified)

  • sort_alias: Sort by (lowest_price, highest_price, most_active, most_recent, ending_soonest, most_profitable, most_relevant)

  • page_number: Page number (default 1)

  • page_size: Results per page, 1-100 (default 30)

  • response_format: "markdown" (default) or "json"

Returns: Paginated list of Flippa listings with price, revenue, profit, bid count, and verification status. Includes pagination metadata with has_more flag.

Examples:

  • Search all open SaaS listings: { "property_type": "saas" }

  • Most profitable websites: { "property_type": "website", "sort_alias": "most_profitable" }

  • Ending soon auctions: { "sale_method": "auction", "sort_alias": "ending_soonest" }

flippa_get_listingA

Get full details for a specific Flippa listing by ID.

Args:

  • listing_id: The Flippa listing ID (e.g., "12299903"). Required.

  • response_format: "markdown" (default) or "json"

Returns: Complete listing details including price, revenue, profit, traffic stats, seller info, verification status, description, and more.

Examples:

  • Get listing details: { "listing_id": "12299903" }

  • Get as JSON: { "listing_id": "12299903", "response_format": "json" }

flippa_analyze_listingA

Analyze a Flippa listing's valuation, compute financial metrics, and assess risk.

This is a computed tool that fetches listing data and calculates valuation metrics including revenue multiples, profit multiples, ROI estimates, and risk factors.

Args:

  • listing_id: The Flippa listing ID to analyze (e.g., "12299903"). Required.

  • response_format: "markdown" (default) or "json"

Returns: Computed analysis including:

  • Financial metrics: revenue/profit multiples, annual revenue, price per visitor, ROI estimate

  • Verdict: "underpriced" (<2x revenue), "fair" (2-4x), "overpriced" (>4x), or "insufficient_data"

  • Risk factors: unverified revenue/traffic, low traffic, no bids, missing images, etc.

Examples:

  • Analyze a listing: { "listing_id": "12299903" }

  • Get analysis as JSON: { "listing_id": "12299903", "response_format": "json" }

flippa_comparable_salesA

Find comparable listings on Flippa for valuation comparison.

If a listing_id is provided, fetches that listing first and uses its property_type and revenue range (0.5x-2x) to find similar listings. You can also search by property_type directly.

Args:

  • listing_id: Find comparables for this listing. Optional.

  • property_type: Filter by type. Overrides the target listing's type if both provided. Optional.

  • page_size: Number of comparables to return, 1-20 (default 10)

  • response_format: "markdown" (default) or "json"

Returns: Target listing (if listing_id provided), list of comparable listings, average price, median price, average revenue multiple, and price range.

Examples:

  • Comps for a listing: { "listing_id": "12299903" }

  • SaaS comparables: { "property_type": "saas", "page_size": 15 }

flippa_market_overviewA

Get aggregate market statistics from the Flippa marketplace.

Queries listings across property types to build a market snapshot including total counts, price/revenue/profit statistics, and verification rates.

Args:

  • property_type: Focus on a specific type (website, saas, ecommerce_store, etc.). If omitted, aggregates across all major types. Optional.

  • response_format: "markdown" (default) or "json"

Returns: Market overview with total listings, breakdown by property type (count, avg price, avg revenue), price/revenue/profit statistics (min, max, avg, median), average revenue multiple, and verified revenue percentage.

Examples:

  • Full market overview: {}

  • SaaS market overview: { "property_type": "saas" }

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.2/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: analyze_listing focuses on valuation metrics and risk assessment, comparable_sales finds similar listings for comparison, get_listing retrieves detailed listing data, market_overview provides aggregate statistics, and search_listings enables browsing and filtering. The descriptions clearly differentiate their functions, eliminating any potential for confusion.

Naming Consistency5/5

All tools follow a perfect 'flippa_verb_noun' pattern (e.g., flippa_analyze_listing, flippa_comparable_sales). This consistent naming convention makes the tool set predictable and easy to understand, with no deviations in style or structure across all five tools.

Tool Count5/5

With 5 tools, this server is well-scoped for analyzing and browsing Flippa marketplace data. Each tool serves a unique and essential function—from individual listing analysis to market-wide insights—without being overly sparse or bloated, making the count ideal for the domain.

Completeness4/5

The tool set covers core workflows for Flippa analysis: retrieving listings (get_listing, search_listings), evaluating them (analyze_listing, comparable_sales), and understanding market context (market_overview). A minor gap is the lack of tools for interacting with listings (e.g., bidding or saving), but this is reasonable for a read-only analysis server focused on data retrieval and assessment.

Maintenance

ActivityInactive
ResponsivenessNo issues