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jeda-ai

by ai.jeda

Generate Framework Analysis

generate_framework_analysis
Read-only

Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph.

ALWAYS call recommend_framework FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404.

REQUIRED post-processing on success: the response's first text block contains a curl -fsSL save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose.

Input: framework_id (namespaced slug from recommend_framework) + user_inputs object (per-framework field set), OR a free-text raw_prompt fallback.

Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
themeNoTheme preset for the SVG render. Default: pastel.
raw_promptNoOptional free-text prompt that REPLACES the auto-derived prompt.
user_inputsNoObject whose keys match the framework user-input form fields.
framework_idYesNamespaced framework slug (e.g. "cms_swot-analysis"). Get one from `recommend_framework` -- bare slugs like "swot-analysis" are not valid ids.

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation (which indicates no state change), the description details the tool's generative nature, input acceptance modes (structured vs raw_prompt), and the exact output format including the SVG save-directive. It also warns about 404 errors for incorrect framework_ids.

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?

Front-loaded with the tool's main action, followed by critical usage guidelines and an example. Every sentence serves a purpose with no redundancy. The structure leads with the most important information.

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 the complexity (nested objects, multiple input modes, SVG generation) and the absence of an output schema, the description thoroughly explains the output format and required post-processing. It also covers input variations and error conditions.

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

Parameters5/5

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

With 100% schema coverage, the description still adds significant value by explaining the relationship between framework_id and user_inputs, the fallback raw_prompt, and the theme preset. It clarifies that user_inputs keys must match framework form fields, which the schema alone does not convey.

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 it 'Runs a specific framework against a user inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation.' It also names the specific knowledge graph and distinguishes itself from siblings like generate_diagram and recommend_framework by focusing on framework analysis generation.

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?

Explicitly instructs to call recommend_framework first to get a valid framework_id, explains id namespacing, warns that bare slugs 404, and provides required post-processing for the SVG file. Includes a concrete example with correct usage.

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
Disambiguation3/5

Tools are mostly distinct but generate_diagram is a deprecated alias for generate_visual, creating potential confusion. Additionally, generate_framework_analysis and generate_visual both produce SVG visuals, though their intended use cases differ. An agent might struggle to choose between them without context.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: generate_diagram, generate_framework_analysis, generate_visual, get_style_options, recommend_framework. The naming is predictable and clear.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose of generating visuals and framework analyses. Each tool serves a distinct role without unnecessary duplication.

Completeness4/5

The tool surface covers the main workflows: framework recommendation, analysis with visual output, general visual generation, and style configuration. Minor gaps like lack of a tool to list all frameworks or manage past outputs exist, but core functionality is covered.

Resources