Get the full detail for a single B2B lead: diagnosis, pitch, follow-ups and opportunity score. Use it before reaching out to a lead. Read-only and free. Admin only.
| Name | Required | Description | Default |
|---|---|---|---|
| leadId | Yes | The lead 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 declare readOnlyHint=true and openWorldHint=false. The description adds significant behavioral context beyond annotations: it confirms the tool is free (no cost), requires admin privileges, and provides the specific fields returned. The annotations already signal a safe read operation, and the description enriches this with operational constraints that affect invocation decisions.
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 three sentences, each earning its place: first sentence says what it does and what data it returns, second sentence explains the use case, third sentence declares constraints (read-only, free, admin-only). No filler words, front-loaded with the core action, immediately actionable.
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 a single required parameter (100% schema coverage) and an output schema (which renders return-value explanation unnecessary), the description is complete. It covers purpose, usage context, behavioral constraints, and parameter discovery. Nothing meaningful is missing for an agent to make an informed invocation decision.
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%, so the schema already documents the single leadId parameter. The description adds minimal parameter-specific info (just that the leadId identifies a single lead), but the high coverage baseline is 3. The description suggests the leadId should come from a listing tool (list_b2b_leads), which adds indirect semantic value beyond the schema definition, justifying a 4.
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 uses specific verbs ("Get the full detail") and names the resource explicitly ("single B2B lead"), listing the exact components returned (diagnosis, pitch, follow-ups, opportunity score). In the sibling list, there is a list_b2b_leads which lists leads, so get_b2b_lead clearly distinguishes itself as the detail-retrieval counterpart.
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 provides excellent usage guidance: it states when to use the tool ("before reaching out to a lead"), implicitly clarifies when not to use it (e.g., use list_b2b_leads to find leads first), and even specifies constraints ("Admin only") and safety properties ("Read-only and free"). This goes beyond basic purpose to actively guide agent decision-making.
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.
