mcp server
Fodda Topic & Trend Research
Trend, stats & insight search across PSFK expert graphs with citable sources.
Description as published by the maintainer. Source
- version 1.33.0
- active
- retrieval
active — Most recent push to the repository was 2026-08-06. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
The endpoint answered, but not with a valid Model Context Protocol response (tools/list rejected: Session not found). The functions below are the last successful declaration, not a fresh answer.
Endpoint status observed on . Source: https://mcp.fodda.ai/topic-research.
15 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.
check_supplemental_status(job_id)- 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. Required: job_id.
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_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_supplemental_context(geo, query, brands, domain, userId, graph_ids)- 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. Price: $10 per query. Required: query.
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.
read_url(url, userId)- 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. Price: $20 per URL lookup. Required: url.
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.
search_insights(limit, query, types, userId, graph_id, min_score)- NARRATIVE only: expert quotes, editorial analysis, and strategic perspectives on a topic — sourced from named strategists and industry leaders. Returns qualitative evidence (quotes, interpretations) with source attribution and parent trend context, NOT raw numbers. For hard data points, market sizes, and growth rates, use search_statistics instead. Works on ALL graphs. Use when you need authoritative voices, strategic framing, or analytical depth that web search cannot provide. Price: $0.50 per search. Required: graph_id, query.
search_statistics(limit, query, userId, graph_id, min_score, include_signals)- HARD NUMBERS only: specific figures, market sizes, growth rates, and quantitative data points across Fodda's knowledge graphs. Each result links back to the expert trend it supports. Use when a question asks for a number or statistic — try this BEFORE supplemental data tools, as Fodda's experts may have already curated the answer. For expert quotes, editorial analysis, and narrative interpretation, use search_insights instead. Works on ALL graphs — domain, expert, and report. Search multiple graphs for best coverage. Price: $0.50 per search. Required: graph_id, query.
Last successful function declaration observed on . Source: https://mcp.fodda.ai/topic-research. 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 | 15 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 | protocol_error | The endpoint answered, but not with a valid Model Context Protocol response (tools/list rejected: Session not found). | as of probe | mcp.fodda.ai |
Where to get it
Related, by what their authors tagged them
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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
-
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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