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Fodda Deep Research

by ai.fodda

search_graph

Read-only

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExecution mode: "research" for topic research, "compare" for upload & compare intelligence. Defaults to "research".research
limitNoMaximum number of results (default 10, max 50)
queryYesThe search query. Country/regional terms filter results at the macro level. Note: Knowledge graph trends are indexed at country/global scope — for sub-national or city-level data (e.g., "US coastal cities"), also query get_supplemental_context.
graphsNoOptional explicit graph scope: an array of graph IDs. When provided, the search is restricted to EXACTLY these graphs — no fallback routing to other graphs. Graph IDs that are unknown, not live, or not yet synced are reported back in `unavailable_graphs` with a reason. Takes precedence over graphId.
userIdNoOptional user identifier for trial usage tracking.
graphIdNoOptional graph ID. If omitted, searches ALL accessible graphs. 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', 'delta/the-connection-index'
skip_skillsNoIf true, skip applying any enabled search enhancement skills for this query only. Use when you want raw, un-enhanced graph results. Default: false.
use_semanticNoWhether to use semantic search (default true)
include_evidenceNoIf true, batch-fetch supporting evidence articles inline with results. Default: true.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description reveals an important behavior: omitting graphId searches all accessible graphs in parallel. It also sets expectations on the result content (cited evidence, source attribution, lifecycle stage) and explicitly distinguishes it from generic web summaries. This is meaningful, non-obvious behavioral detail.

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?

The description is front-loaded with the core purpose and return value, then adds the key behavior guidance, then the use-case list. Every sentence adds meaningful information and there is no filler or repeated schema content.

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?

For a tool with 9 parameters and no output schema, the description covers the output shape, the main behavioral default, the use boundaries (national/global vs sub-national), and when to delegate to a sibling. Combined with the comprehensive schema, it is complete enough for an agent to call it 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?

The input schema has 100% description coverage across all 9 parameters, so the baseline is 3. The description adds a little extra by recommending the default graphId behavior, but most parameter meaning is already carried by the schema, so the description doesn't need to compensate.

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 states a specific verb and resource — 'Find trends, signals, and expert insights across 100+ curated knowledge graphs' — and clearly differentiates this tool from generic web search by mentioning cited evidence, source attribution, and lifecycle stage. It is immediately clear what type of task this tool is for.

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

Usage Guidelines5/5

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

It explicitly says to use this tool for 'market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts' and gives the default recommendation to omit graphId to search all accessible graphs. It also points to get_supplemental_context when sub-national or city-level data is needed, which is strong when-vs-alternative guidance.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct roles: graph discovery, graph search, node detail, neighbor mapping, evidence retrieval, deep research launch, and status polling are all identifiable. The main ambiguity is between search_graph and get_label_values(label='Trend') for listing trends, and between deep_research_topic's built-in supplemental coverage and get_supplemental_context.

Naming Consistency4/5

The set largely follows a snake_case verb_noun pattern such as search_graph, list_graphs, get_node, read_url, and generate_visual. The outlier is deep_research_topic, which is noun-led rather than verb-led, and get_my_account is a minor deviation from the pure verb_noun pattern, but neither seriously disrupts usability.

Tool Count5/5

14 tools is well-scoped for a deep-research platform: graph discovery, graph search, retrieval, evidence, supplemental data, status polling, visualization, URL ingestion, and account/capability helpers all earn their place. The set feels like a deliberate pipeline rather than a miscellany.

Completeness4/5

The research lifecycle is well covered: discover graphs, search, explore trends, get supporting evidence, add supplemental data, launch deep research, poll status, and generate visuals. Minor conveniences like canceling or listing past research sessions are missing, but there are no dead ends for the core workflow.

Resources