Fodda Earnings Intelligence

{
  "id": "aix_b39f73d5b3",
  "name": "Fodda Earnings Intelligence",
  "operator": "(unclaimed - source: registry-official · publisher: ai.fodda)",
  "description": "Cross-company earnings trends & executive divergence with citable sources.",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-09-25",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "citation-check",
    "sentiment-scan"
  ],
  "protocols": {
    "mcp": "https://mcp.fodda.ai/earnings-intelligence",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 1468,
    "uptime_30d": 0.9890710382513661,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 43,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-09-25T05:01:24.158Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://www.fodda.ai/"
    },
    {
      "label": "repository",
      "url": "https://github.com/piers-fawkes/fodda-mcp"
    }
  ],
  "avatar": "https://github.com/piers-fawkes.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/piers-fawkes"
    }
  ],
  "profile": {
    "mcp_server": "fodda_mcp",
    "mcp_version": "1.46.48",
    "tool_count": 13,
    "tools": [
      {
        "name": "get_my_account",
        "description": "Check the current user's account status: API call balance, plan, enabled/disabled graphs, and profile info. Use when the user asks \"how many API calls do I have?\", \"what plan am I on?\", \"what graphs can I access?\", or similar account questions. Returns live data — not cached from session start."
      },
      {
        "name": "list_graphs",
        "description": "List all expert knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts, and topic coverage (e.g. retail, tech, food, travel, fashion, beauty, sports). Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools."
      },
      {
        "name": "get_capabilities",
        "description": "Returns Fodda's main capabilities / features / offerings / products / services / tools and how to use them. Call this for any question about what Fodda can do or what's available."
      },
      {
        "name": "search_graph",
        "description": "Find trends, signals, and expert insights across 100+ curated knowledge graphs covering retail, beauty, tech, food, travel, sports, and 30+ specialist domains. Returns trend data with cited evidence, source attribution, and lifecycle stage (emerging/building/mature/fading) — not generic web summaries. If graphId is omitted, searches ALL accessible graphs in parallel (recommended default). Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts, or any topic where curated expert intelligence outperforms web search."
      },
      {
        "name": "get_neighbors",
        "description": "Discover what's connected to a specific trend — related brands, technologies, locations, and cross-domain links that search alone wouldn't surface. Returns curated editorial connections between trends that web search cannot provide. Use after search_graph to map the territory around a trend, find which brands are connected, or understand cross-domain relationships. Requires node_id from a prior search_graph result."
      },
      {
        "name": "get_evidence",
        "description": "Get the source articles, case studies, and statistics behind a specific trend — with full citations and publisher attribution. Each item includes source URL, location, brand names, publication date, category, and a formatted citation. Use after search_graph when you need the supporting proof behind a trend. This is a direct lookup by trend ID — not a text search tool."
      },
      {
        "name": "get_node",
        "description": "Get the full profile of a specific trend — detailed description, lifecycle stage (emerging/building/mature), signal strength, geographic scope, and all properties. Use when you need deeper detail on a single trend after search_graph returned a summary. Requires node_id from a prior search_graph result."
      },
      {
        "name": "get_label_values",
        "description": "List all brands, locations, technologies, audiences, or trends within a specific knowledge graph. Use to explore what a graph contains — e.g., \"what brands are in the retail graph?\" or \"what locations does the fashion graph cover?\". To get a complete list of every trend in a graph, call with label=\"Trend\" — this returns the full deterministic list, useful for industry-report graphs where search may return partial results."
      },
      {
        "name": "get_earnings_intelligence",
        "description": "Cross-company thematic earnings intelligence from the knowledge graph and web sources. Use for multi-company comparisons (\"what are hotel companies saying about labor costs?\"), industry-level queries, or sector filters. For single-brand earnings, brand_tracker includes earnings automatically. For per-ticker structured analysis (analyst concerns, activity breakdown, validated consumer trends), use get_company_earnings instead — it reads the canonical truth layer. Results may include \"knowledge_graph\" or \"web_supplemental\" provenance."
      },
      {
        "name": "get_earnings_divergence",
        "description": "Cross-company analyst-management divergence detection from the knowledge graph (legacy-thematic). Surfaces where executives are deflecting, reframing, or avoiding specific topics — the gap between what analysts press on and how management responds. Use for \"where are executives deflecting?\" or \"divergence in [sector] earnings.\" For per-ticker deflection signals, use get_company_earnings with view=qa and filter by response_directness."
      },
      {
        "name": "get_company_earnings",
        "description": "The canonical per-ticker earnings source. Returns the full truth-layer record for covered tickers (517 consumer-sector companies) — analyst concerns, sentiment labels, strategic activity (marketing/retail/technology/sustainability), CEO intelligence, and validated consumer trends from Fodda's quarterly analysis pipeline. Falls back to web-backfill for uncovered tickers. Use this for company-specific data. Use get_earnings_intelligence for cross-company thematic comparisons."
      },
      {
        "name": "get_validated_trends",
        "description": "Returns market-validated consumer trends from corporate earnings reports cross-validated by Fodda's analysis pipeline. Connects earnings commentary (analyst concerns, CEO statements) with consumer trend signals."
      },
      {
        "name": "generate_visual",
        "description": "Create a presentation-ready data visualization from research findings. Available chart types: \"cultural_shifts\" (From→To transitions), \"competitive_compass\" (brands on 2 axes), \"trend_constellation\" (network of related trends), \"implication_ladder\" (Signal→Trend→So What→Do What), \"innovation_pathway\" (Now→Near-Term→Future), \"opportunity_map\" (2×2 white space analysis). Returns a branded SVG that renders directly in the chat. Highlight focal entity using top-level \"focus\":\"Name\" or per-item \"focus\":true."
      }
    ],
    "profiled_at": "2026-09-02T21:01:05.572Z"
  },
  "unreachable": false
}