mcp server
Fodda Earnings Intelligence
Cross-company earnings trends & executive divergence with citable sources.
Description as published by the maintainer. Source
- version 1.33.0
- active
active — Most recent push to the repository was 2026-08-06.
What this server can do
The endpoint published in the registry did not answer (HTTP 503). The listing points at a server that is not running. The functions below are the last successful declaration, not a fresh answer.
Endpoint status observed on . Source: https://mcp.fodda.ai/earnings-intelligence.
13 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
generate_visual(data, chart_type)- 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. Required: chart_type, data.
get_capabilities(userId)- Returns Fodda's main capabilities / features / offerings / products / services / tools and what they cost. Call this for any question about what Fodda can do or what's available.
get_company_earnings(mode, view, period, sector, ticker, userId, analyst, metrics, tickers)- 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. Price: $20 per query (coverage view is free). Use this for company-specific data. Use get_earnings_intelligence for cross-company thematic comparisons.
get_earnings_divergence(limit, dateTo, search, sector, userId, dateFrom, industry)- 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. Price: $20 per query.
get_earnings_intelligence(brand, limit, dateTo, search, sector, ticker, userId, dateFrom, industry)- 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. Price: $30 per query.
get_evidence(top_k, userId, graphId, for_node_id)- 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. Price: $0.50 per lookup. Required: graphId, for_node_id.
get_label_values(label, userId, graphId, property)- 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. Required: graphId, label.
get_my_account- 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.
get_neighbors(depth, limit, userId, graphId, direction, seed_node_ids, relationship_types)- 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. Required: graphId, seed_node_ids.
get_node(nodeId, userId, graphId)- 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. Required: graphId, nodeId.
get_validated_trends(limit, search, sector, ticker, userId)- 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. Price: $25 per query.
list_graphs(userId)- 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.
search_graph(mode, limit, query, userId, graphId, skip_skills, use_semantic, include_evidence)- 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. Price: $20 per query. Required: query.
Last successful function declaration observed on . Source: https://mcp.fodda.ai/earnings-intelligence. We list what the server declared; we do not call any of these functions.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 1 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-06 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.33.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-27 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-07-27 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 13 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | mcp.fodda.ai | |
| mcp endpoint status | unreachable | The endpoint published in the registry did not answer (HTTP 503). The listing points at a server that is not running. | as of probe | mcp.fodda.ai |
Where to get it
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These share tags the maintainers applied themselves, such as agentic, copilot, knowledge-graph, research. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
Also from piers-fawkes
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Fodda Knowledge Graphs
Expert-curated knowledge graphs for AI agents — retail, beauty, sports, plus institutional data
How the author describes it
Topics the maintainer set on GitHub: agentic, ai-agents, claude, copilot, cursor, knowledge-graph, mcp, model-context-protocol, research, retail, trend-intelligence.
This record as data
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