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Fodda Earnings Intelligence

get_node

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIdYesThe 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.
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'

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description only needs to add context beyond that. It does so by describing the profile fields returned and the dependency on a prior search_graph result. It does not mention possible operation behaviors like rate limits or result size, but the safety profile is already captured and the description is accurate to those annotations with no contradiction.

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?

Two sentences, no fluff. The first sentence contains the action and output outline; the second contains the call trigger and dependency. Every sentence serves a purpose with no redundant phrases.

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

Completeness4/5

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

The description is well-formed for a read-only retrieval tool with annotations for safety. It gives the trigger and bound key, name and dependency and enough content in terms of what is returned. Since there is no graph known, it could be a little more explicit about how graphId coordinates with nodeId, but the schema fills most of that gap.

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 description coverage is 100%, so the baseline under the rubric is 3. The description repeats the node_id prerequisite that is also well documented in the schema (node must come from prior search result), but adds no meaning beyond the schema. graphId and userId are not described in the description, but the schema itself carries enough.

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 starts with a clear action and resource: "Get the full profile of a specific trend" and then enumerates the content returned (description, lifecycle stage, signal strength, geographic scope, and properties). It clearly differentiates from sibling tools by placing this as the deeper detail tool that operates on a trend node from search_graph, not on earnings, labels, capabilities, or other resources.

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?

Explicit usage guidance is present: "Use when you need deeper detail on a single trend after search_graph returned a summary." It also states the prerequisite: "Requires node_id from a prior search_graph result," which tells an agent when it is appropriate and what it needs before invoke. This is far beyond a mere hint.

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

A4.4/5.0
Disambiguation4/5

Most tools have distinctly different jobs: graph discovery, per-ticker earnings, cross-company earnings intelligence, evidentiary lookup, account status, and visualization. There is some overlap among get_validated_trends, get_company_earnings, and get_earnings_intelligence, but the descriptions provide enough routing guidance to prevent most misselections.

Naming Consistency5/5

Tool names consistently follow a verb_noun pattern: get_*, search_*, list_*, generate_*. Naming is predictable and the object of each verb is clear, making the API surface easy to navigate.

Tool Count5/5

Thirteen tools is well-scoped for a research/earnings intelligence server. Each tool covers a distinct part of the workflow from authentication and graph discovery to deep node exploration and presentation output.

Completeness5/5

The tool set is complete for its presumed read/research-only domain. It offers graph discovery, trend lookup, evidence retrieval, per-company earnings records, cross-company comparisons, divergence analysis, account status, and output visualization. No major workflow dead-end is apparent.