Return the revenue projection for an idea: financial model, growth scenarios and unit economics. Use it to sanity-check whether the business can make money before investing further. Read-only and free; pass an ideaId you own.
| Name | Required | Description | Default |
|---|---|---|---|
| ideaId | Yes | The idea ID |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes | The tool result rendered as human and AI readable text or markdown. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description reinforces 'Read-only and free', confirming no side effects. It also adds a critical behavioral constraint: 'pass an ideaId you own,' which informs the agent about access permissions beyond what the schema provides. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no wasted words. The first sentence states what the tool returns, the second provides usage guidance. Every sentence adds distinct value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter, has an output schema (so return values are documented separately), and annotations cover safety, the description fully covers what an agent needs: purpose, usage guidance, the fact it's read-only and free, and the ownership requirement. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter (ideaId) described in the schema. The description adds meaningful context by stating the ideaId must be owned by the caller, adding a usage constraint not in the schema. This provides value beyond the base baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a revenue projection for an idea, specifying three concrete elements: financial model, growth scenarios, and unit economics. This directly distinguishes it from tools like get_competitive_density, get_market_signals, get_moat_analysis, and other get_* siblings that return different types of analyses about an idea.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when to use this tool: 'Use it to sanity-check whether the business can make money before investing further.' This gives clear context. However, it does not explicitly mention when NOT to use it or name alternative tools for other financial checks, such as get_investment_memo or get_founder_dna.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct function or data aspect, from idea CRUD to simulations, content generation, and team management. Despite the large number, descriptions clearly differentiate purposes, e.g., 'get_idea_summary' vs. 'get_idea_agents' vs. 'get_idea_evolution'. No two tools appear to do the same thing.
All tools follow a consistent verb_noun pattern in snake_case (e.g., 'create_idea', 'get_competitive_density', 'toggle_favorite'). No mixing of conventions like camelCase or abbreviations. The pattern is uniform and predictable.
63 tools is far beyond the typical well-scoped range of 3-15. While the platform's broad scope (idea validation, B2B, team, simulations) justifies many, the sheer volume can overwhelm an agent. A more curated subset or grouping would improve coherence.
The tool set covers the full startup idea lifecycle: creation, validation, retrieval of various analyses, updates, deletion, sharing, simulations, B2B lead generation, team collaboration, and market intelligence. No obvious gaps exist for the stated domain.
