Read the full markdown text of one article. Accepts the article slug (e.g. "wallet-drainer-anatomy") or its site path (/articles/... or /zh/articles/...).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article slug or path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly identifies this as a read operation returning markdown text and discloses the accepted path variants. It doesn't cover error behavior, but for a simple read tool the core behavior is transparent.
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, front-loaded with action and output, followed by concise input examples. Every phrase contributes.
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 read tool with no output schema, the description states what is returned and what input is accepted. It omits error/not-found behavior and alternative-tool routing, but those are minor gaps for this level of complexity.
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 documents the single slug parameter at 100% coverage, so the baseline is 3. The description adds value with a concrete slug example and the exact path formats (/articles/... and /zh/articles/...), going beyond the schema's one-line description.
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?
States a specific verb (read) and resource (article), and adds the output format (full markdown text). It doesn't explicitly differentiate from siblings like read_glossary or list_content, so it stops short of a 5.
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 when to use it—when you need the full markdown of one article—and provides accepted input forms. It gives no explicit guidance about when not to use it or how it compares to list_content, read_glossary, or search.
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 serves a clearly distinct purpose: listing content, reading an article, reading a glossary term, and searching across content. There is no overlap or ambiguity between them, even for similar operations like reading an article vs. reading a glossary.
All tool names follow a consistent snake_case verb_noun pattern (list_content, read_article, read_glossary, search). While 'search' is a single verb, it is a common exception and the overall pattern is uniform and predictable.
Four tools is a well-scoped set for a content access server. Each tool covers a distinct access mode (list, read, search) without redundant or unnecessary additions, making the surface compact yet functional.
The tool surface fully covers the server's purpose: browsing the table of contents, retrieving articles and glossary terms, and searching across both languages. There are no obvious gaps in the core workflow of discovery and reading.