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jeda-ai

by ai.jeda

Recommend Framework

recommend_framework
Read-onlyIdempotent

Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated).

Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched.

Input: just the problem statement is enough. Optional faceted filters (persona, regulation, decision_stage) narrow the candidate set. Set limit between 3 and 10 for picker UIs.

Pair with generate_framework_analysis to actually run a recommended framework against the user's inputs.

Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of frameworks to return. Default 5; cap 20.
personaNoPersona slug (e.g. "ciso", "pm", "cfo").
regulationNoRegulation slug (e.g. "sox", "gdpr", "iso-31000").
decision_stageNoDecision-lifecycle stage.
problem_statementYesPlain-English description of the business problem. 10–2000 chars.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds that it returns top-N frameworks ranked by fit with concrete reasons citing specific problem signals matched. It also mentions the knowledge graph is 'edge-curated', adding trust and behavioral context.

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 concise (4-5 sentences) and well-structured: purpose first, then data source, usage guidelines, parameter details, pairing advice, and an example. Every sentence adds value with no redundancy.

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?

Despite having no output schema, the description explains the return format (ranked list with fit scores and reasons), input requirements, optional filters, and includes an example. It covers all necessary context for an agent to use 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 coverage is 100%, so baseline is 3. The description adds practical guidance: 'Input: just the problem statement is enough', 'Set limit between 3 and 10 for picker UIs', and explains faceted filters narrow candidates. This provides valuable usage nuance beyond the schema descriptions.

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 recommends business/strategy/risk frameworks for a stated problem, distinguishing it from siblings like `generate_framework_analysis`. The specific verb 'recommends' and resource 'framework knowledge graph' make the purpose unambiguous.

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?

The description explicitly says 'Use when the user describes a business problem... rather than naming a specific framework' and advises to pair with `generate_framework_analysis` to run the recommendation. It also notes optional filters and limit, providing clear context for when and how to use this tool versus alternatives.

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