Fodda Deep Research

{
  "id": "aix_b4cf78f9e4",
  "name": "Fodda Deep Research",
  "operator": "(unclaimed - source: registry-official · publisher: ai.fodda)",
  "description": "Autonomous deep research reports merging PSFK trend graphs with citable sources.",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-09-24",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "citation-check",
    "market-data",
    "media-catalog",
    "web-research"
  ],
  "protocols": {
    "mcp": "https://mcp.fodda.ai/deep-research",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 1544,
    "uptime_30d": 0.9945054945054945,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 47,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-09-24T22:01:16.360Z"
  },
  "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": 14,
    "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_supplemental_context",
        "description": "A standard layer for macro, institutional, and real-time market data. Call this tool when curated coverage is thin, empty, or when the query is explicitly demand/attention-shaped (e.g. to get search volume, economic series, or census data). It retrieves data from 80+ authoritative sources (Google Trends, FRED, BLS, Census, etc.) fanned out in parallel. Returns categorized data blocks with source attribution and metadata. Note: call after search_graph indicates thin/empty coverage via its coverage annotation."
      },
      {
        "name": "check_supplemental_status",
        "description": "Check if market data gathering is complete and retrieve the results. Call this after get_supplemental_context — poll every 5-10 seconds until status is COMPLETE or FAILED."
      },
      {
        "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."
      },
      {
        "name": "read_url",
        "description": "Extract clean text content from any URL. Use this when a user shares a link (competitor site, news article, client brief, trend report) and wants to cross-reference it against Fodda knowledge graphs. Returns structured text ready for analysis."
      },
      {
        "name": "deep_research_topic",
        "description": "Launch an autonomous Deep Research session that combines Fodda knowledge graph intelligence with live web research to produce a comprehensive editorial-quality report. The Research Agent plans its own strategy, searches multiple graphs, validates with institutional data, and synthesizes into a narrative brief with inline source citations. Use for complex, multi-faceted questions that need both curated expert intelligence AND current web context — e.g., strategic briefings, market landscape reports, competitive deep dives. Automatically includes earnings-call intelligence and macro/supplemental"
      },
      {
        "name": "check_research_status",
        "description": "Check if deep research is complete and retrieve the final report. Call this after deep_research_topic — poll every 10 seconds until status is COMPLETE or FAILED."
      }
    ],
    "profiled_at": "2026-09-02T21:01:05.414Z"
  },
  "unreachable": false
}