Fetch one ChangeGamer guide (editorial article) by slug as full Markdown with its metadata header. Always free — never part of the paid corpus. Use list_articles to discover slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article slug (e.g. "rag-in-production") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears the full burden. It provides an important behavioral guarantee (always free, not in paid corpus) and shares the output format (full Markdown with metadata header). Error behavior is missing, but for a read-only fetch this is largely sufficient.
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 very concise with no over-explanation. All three components — purpose, output details, and slug discovery hint — are front-loaded and directly informative, making it fast for the agent to parse.
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 has no output schema, so the description correctly mentions the value format (Markdown + metadata header). With a single required parameter and a clearly captured free and discovery behavior, this is good coverage for an 1-input fetch operation.
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 coverage is 100%, and the slug is already well-described with an example in the schema. The description adds extra context beyond the schema by telling the agent that slugs are discoverable via list_articles, which improves accurate parameter selection.
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 names the resource: a single ChangeGamer guide (editorial article) by slug, returned as full Markdown with a metadata header. It distinguishes the tool from list/get resource siblings by emphasizing the article type and one-item scope.
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?
It explicitly instructs to use list_articles for slug discovery, which sets up a clear 'use get_article once you have a slug' workflow. It does not enumerate all alternative tools or exclusions (e.g., search_resources), but it provides a concrete usage tip within the tool itself.
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 targets a distinct action: checking access, fetching corpus (free vs full), pricing, payment, listing, searching, stats, and single resource retrieval. No significant overlap.
All tools follow a consistent verb_noun pattern in snake_case (e.g., check_access, get_corpus, list_resources). Verbs are uniform and descriptive.
With 10 tools, the set is well-scoped for a resource access and management API. Each tool serves a clear purpose without redundancy or excessive number.
The toolset covers core operations: access verification, corpus retrieval (free and premium), pricing, payment info, resource listing, search, and stats. Missing write operations (e.g., purchase), but acceptable for a read-focused server.