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Fodda Topic & Trend Research

by ai.fodda

get_evidence

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoNumber of evidence items to return (default 5)
userIdNoOptional user identifier for trial usage tracking.
graphIdYesThe graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'dentsu-creative-marketing', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'
for_node_idYesThe node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name.

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds concrete behavior beyond that by detailing what each returned evidence item contains, including source URL, brand names, publication date, and formatted citation. Since there is no output schema, this compensates well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences: what it does, what it returns, and when/how to use it. No fluff, and the critical usage constraint is clearly placed near the end for emphasis.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately covers the return values by listing the item fields. Required parameters are fully documented in the schema, annotations handle safety and idempotency, and usage context references search_graph appropriately. Nothing critical is missing for the agent to invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already explains graphId, for_node_id, top_k, and userId. The description adds the useful framing that this is 'a direct lookup by trend ID,' but it does not materially extend the parameter semantics already documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves source articles, case studies, and statistics for a specific trend, with citations and publisher attribution. It also explicitly distinguishes itself from a text search tool, making its direct-lookup-by-ID role unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear when-to-use guidance ('Use after search_graph when you need the supporting proof behind a trend') and excludes text-search usage. It does not enumerate all sibling alternatives or contrast against search_insights/search_statistics, but the context is strong enough for an agent to select it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation4/5

The tools are mostly distinct: search_graph, search_insights, search_statistics, and get_supplemental_context are carefully differentiated by output type, and graph exploration/evidence tools have clear roles. A couple of retrieval tools (get_validated_trends vs search_graph, search_statistics vs get_supplemental_context) could be mistaken at a glance, though their descriptions do enough to separate them.

Naming Consistency5/5

All tool names use a consistent lowercase snake_case verb_noun pattern (search_*, get_*, list_*, generate_*, check_*, read_*). Verbs map predictably to actions, and there are no mixed conventions or vague generic names.

Tool Count5/5

Fifteen tools is at the upper edge of the ideal range but each one maps to a distinct research workflow step: discovery, graph search, targeted retrieval, evidence, supplemental data, visualization, and account/capability checks. The breadth is justified by the server's broad trend-research scope.

Completeness5/5

The surface covers the full read-only research lifecycle: list graphs, search across them, drill into nodes/neighbors/evidence, get quantitative and qualitative answers, supplement thin coverage with external data, and produce visuals. Meta tools for account/capability and URL import prevent dead ends.

Resources