Returns a description of who Fluentive is built for - industries, professions, and business types. Use when the user asks if Fluentive fits their profession or business.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description accurately describes a read-only informational tool with no side effects. It fully discloses the output type (a description) and scope (industries, professions, business types).
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 concise sentences front-load the purpose and usage. No unnecessary words; every sentence adds 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?
For a simple tool with no parameters and no output schema, the description is fully complete: it states the output and when to use it. No additional context needed.
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?
The tool has no parameters, so no parameter descriptions are needed. With 0 parameters, baseline is 4 per guidelines. Schema coverage is irrelevant here.
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 description of the target audience (industries, professions, business types). It includes specific verb 'returns' and resource 'description of who Fluentive is built for', distinguishing it from sibling tools like get_company_info or get_features.
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?
Explicitly says 'Use when the user asks if Fluentive fits their profession or business', providing clear context for when to invoke this tool over others.
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.
Every tool has a clear, distinct purpose with well-written descriptions that prevent confusion. For example, 'get_overview' provides a general summary, while 'get_company_info' focuses specifically on the company behind Fluentive. No two tools have overlapping functionality.
All tools consistently use snake_case, with most following a verb_noun pattern (e.g., get_overview, list_blog_posts, compare_with_alternatives). The verbs are appropriate and uniform in style, making it easy to predict tool names.
With 15 tools, the server is well-scoped for its purpose of providing comprehensive information about Fluentive. Each tool covers a specific aspect, and the number feels neither too sparse nor overwhelming.
The tool surface covers most common user queries: overview, features, pricing, security, FAQ, blog, search, and industry solutions. Minor gaps exist, such as missing dedicated tools for integrations or testimonials, but the overall coverage is strong and agents can navigate effectively.
