Generate high-quality written content. Supports blog posts, product descriptions, documentation, social posts, and emails. Customizable tone, length, and keywords.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Writing tone (default: professional) | |
| type | Yes | Content type | |
| topic | Yes | Topic or subject to write about | |
| length | No | Target length (default: medium) | |
| keywords | No | Comma-separated keywords to include |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations provided, so the description carries the full burden of behavioral disclosure. It does not mention output format, potential costs, quality caveats, or what the function actually returns beyond the generic promise of 'high-quality' content. The description gives no details about how tone, length, and keywords affect the output or any limitations.
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 sentences, front-loaded with the main action, and includes only essential information. Every clause adds value: the action, the supported types, and the customization options. There is no fluff or repetition of schema details.
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 low-to-moderate complexity of a content generation tool, the description is reasonably complete. It covers the main purpose, lists applicable content types, and mentions configurable attributes. There is no output schema or annotations, but the return value (generated text) is easily inferred. A slight deduction for not mentioning any potential limitations or response format.
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 input schema provides full coverage of all five parameters (100%), including descriptions and enums. The description adds no extra semantic value; it merely repeats 'customizable tone, length, and keywords,' which are already documented in the schema. Baseline score of 3 is appropriate since the schema does the heavy lifting.
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's function: 'Generate high-quality written content.' It enumerates specific content types (blog posts, product descriptions, documentation, social posts, emails), which distinguishes it from sibling tools like analyze_sentiment, extract_structured_data, or search_web. The verb 'generate' and resource 'written content' are specific and unambiguous.
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 usage by listing supported content types ('Supports blog posts, product descriptions, documentation, social posts, and emails'), but it does not explicitly state when to use this tool over alternatives or provide any exclusions. Sibling tools are obviously different, but there is no direct 'when to use' or 'when not to use' guidance.
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 has a clearly distinct purpose: sentiment analysis, structured data extraction, content generation, health check, tool listing, code review, web scraping, screenshot, and web search. There is minor overlap between scrape_url and extract_structured_data, but the different outputs (raw text vs. structured JSON) make them easily distinguishable.
Most tool names follow the verb_noun snake_case pattern (e.g., analyze_sentiment, scrape_url). The only outlier is 'health', which is a single noun rather than a verb_noun like 'check_health'. This minor inconsistency slightly reduces coherence.
With 9 tools, the server is well-scoped for a general-purpose utility toolkit. Each tool serves a useful function without redundancy, and the count is within the ideal range (3-15) for clarity and manageability.
The tool set covers a broad range of common AI/automation tasks such as text analysis, web scraping, content generation, search, and code review. While some potential utilities (e.g., translation or file conversion) are missing, the lack of a specific domain makes the set feel reasonably complete for a general-purpose toolkit.