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Fodda Earnings Intelligence

by ai.fodda

generate_visual

Read-onlyIdempotent

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. Highlight focal entity using top-level "focus":"Name" or per-item "focus":true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesJSON string containing chart data. Optional top-level "focus":"Name" or per-item "focus":true highlights key entity in brand accent. cultural_shifts: {shifts:[{from,to}]}. competitive_compass: {brands:[{name,x,y,focus?:boolean}], axes:{left,right,top,bottom}, focus?:string}. trend_constellation: {trends:[{name,x,y,focus?:boolean}], connections:[{from,to,strength}], focus?:string}. implication_ladder: {signal,trend,so_what,do_what}. innovation_pathway: {now,near_term,future}. opportunity_map: {items:[{name,consumer_desire,market_activity,focus?:boolean}], x_label?:string, y_label?:string, focus?:string}
chart_typeYesThe type of visualization to generate

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context beyond annotations: it returns a branded SVG that renders directly in chat, lists the chart-type-specific transitions, and explains the focus highlighting mechanism. No contradiction with annotations.

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 dense but every sentence earns its place: primary purpose, chart type list with meanings, output format, and focus highlighting. It is front-loaded with the main verb and resource, and the chart list is necessary for a tool with six distinct subtypes.

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?

For a tool with 6 chart types and no output schema, the description covers the return format (SVG) and all chart variants. The input schema handles the detailed data structures. Minor gaps remain, such as explicit guidance on when each chart type is most appropriate, but overall the agent has enough context to select and invoke the tool correctly.

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

Parameters4/5

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

Schema description coverage is 100%, with the data parameter's schema description containing detailed per-chart-type JSON formats. The tool description adds semantic meaning beyond the schema by explaining what each chart type represents (e.g., 'cultural_shifts' (From→To transitions), 'opportunity_map' (2×2 white space analysis)), helping the agent choose correctly.

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 opens with a specific verb and resource: 'Create a presentation-ready data visualization from research findings.' It enumerates all six chart types with clarifying parentheticals, clearly distinguishing this tool from the get_*/list/search sibling tools that retrieve data rather than generate visuals.

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?

The phrase 'from research findings' and 'presentation-ready' provide clear context for when to use this tool. It does not explicitly name alternatives or exclusions, but the sibling tools are all data retrieval/search operations, making the use case unambiguous. It could be stronger with an explicit 'use this instead of X when a visual is needed' statement.

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.

Resources