Return social engagement metrics for an idea: views, watchlist count, interest signals and share count. Use it to gauge how much traction an idea has attracted on the platform. 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 destructiveHint=false, so the description adds value by stating 'Read-only and free', confirming no mutation or cost. It also adds that you must pass an ideaId you own, which is contextual safety info. No contradictions 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?
Two sentences: first defines return metrics, second states purpose and access constraints. No extraneous information. Every sentence earns its place.
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 low complexity (1 param, no nested objects, output schema present), the description is complete. It explains what metrics are returned, the use case, and access condition. Could optionally mention that the output schema includes exactly those four fields, but not required.
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 description coverage is 100% (ideaId string) so baseline is 3. The description does not add additional parameter details beyond the schema, but it does set context that the ideaId must be owned by the user, which is a nuance not in the schema.
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 social engagement metrics (views, watchlist count, interest signals, share count) for an idea, and explicitly says it gauges traction on the platform. This differentiates it from siblings like get_demand_proof or get_market_signals by focusing specifically on social proof metrics.
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 implies use for checking traction of an idea, and notes it requires an ideaId you own, but does not explicitly say when not to use it or name alternatives. Sibling tools like get_demand_proof or get_improvement_synthesis could provide similar or complementary data, but no exclusions are given.
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.
