Get metadata for a single article on loopingagent.com: title, category, excerpt, cover image and canonical URL. Article slug can be found with list_articles. Full content is available at the returned URL (server-rendered).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article slug. Ex: browser-automation, agentic-commerce-protocol, langgraph-generative-ui |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It explains that this is a metadata-only operation (not full content) and that full content is server-rendered at the returned URL. This adds meaningful behavior beyond the name and schema.
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?
Three sentences, each earning its place: what the tool returns, where to find the slug, and what to do for full content. The most important information is front-loaded.
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 single-parameter metadata retrieval tool, the description covers the return fields, the key input source, and the full-content route. It doesn't describe error behavior or response structure, but no output schema exists and the operation is simple enough that these omissions are not critical.
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 schema already fully documents the single 'slug' parameter with examples and 100% coverage. The description adds a useful hint that the slug can be found via list_articles, but this is modest additional context rather than substantial semantic enrichment.
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 states a specific verb ('Get') and resource ('metadata for a single article on loopingagent.com'), then enumerates exactly what is returned: title, category, excerpt, cover image, and canonical URL. It clearly distinguishes itself from list_articles by focusing on a single article rather than a listing.
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 tells the agent how to obtain the required 'slug' parameter by referencing list_articles, and it clarifies that full content is available via the returned URL. It doesn't explicitly state alternatives or exclusions, but for a simple retrieval tool the guidance is adequate.
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 maps to a distinct resource and action: booking a call, viewing one article, viewing the profile, viewing skills, and listing articles. While get_profile includes a Cal.com link, book_call is clearly the purpose-specific tool for scheduling, so there is no real ambiguity.
All tool names follow a consistent lower_snake_case verb_noun pattern: book_call, get_article, get_profile, get_skill, list_articles. Singular get_* tools and the plural list_articles follow an intuitive, predictable convention.
Five tools is well-scoped for a personal website/portfolio server. Each tool covers a meaningful surface area without redundant or filler operations.
The toolset covers the core visitor and agent needs: discovering and reading article metadata, retrieving the profile and contact details, assessing skills, and booking a call. Article content is intentionally available via the returned canonical URL, so there are no dead ends.
