read_topicFetch the full Markdown article for a topic slug, with infobox and citations.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| include_sources | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Fetch' clearly signals a non-mutating read operation, and the description states the return shape (Markdown article with infobox and citations). It does not cover behavior for missing slugs, permissions, or the effect of include_sources, but the core behavior is reasonably 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?
The description is a single sentence with no filler, front-loading the verb and object. Every word contributes to understanding what the tool does, which is exactly what a concise tool description should do.
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?
The tool is simple and has an output schema, so return-value detail is not required. However, the description omits the meaning of include_sources and provides no explicit guidance for choosing between this and sibling tools. It is nearly adequate but has clear gaps.
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 0%, so the description must compensate. It explains that the slug identifies the topic, but it never explains include_sources, its boolean meaning, or its default true value. The description leaves half the parameters semantically unexplained.
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 a specific verb ('Fetch') and a specific resource ('full Markdown article for a topic slug'), and mentions the output elements (infobox and citations). This clearly differentiates it from the sibling tools: search_topics discovers topics, while create_topic and edit_section mutate.
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?
Usage is implied: use this when you already have a topic slug and want the full article content. However, it does not explicitly say when to use it instead of search_topics or discover_topics, nor does it mention that those tools should be used to find a slug first.
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.
Most tools map cleanly to distinct actions like create, read, edit, and search. The main ambiguity is between search_topics and discover_topics, though the descriptions clarify keyword vs. semantic retrieval.
All tool names follow a consistent lowercase verb-first pattern: create_, discover_, edit_, list_, preview_, read_, search_. This makes the expected action immediately clear and predictable.
Seven tools is well-scoped for an encyclopedia server, covering search, discovery, reading, creating, editing, demand insight, and pre-authoring previews. Each tool serves a distinct step without unnecessary bloat.
The core lifecycle is covered: discover and read topics, create missing ones, edit sections, and preview phrases before committing. Deletion is absent but is reasonably not part of this shared knowledge-base workflow.