Return the entire free corpus as one document (every free resource title, description, canonical URL, and full Markdown body) — the same content as /llms-full.txt. Premium resources appear as a stub with a purchase link, not their body. Use this to ingest everything in a single call; the response is large.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description clearly discloses the tool's behavior: it returns full content for free resources and stubs for premium resources, and notes the large response size. This sufficiently informs the agent of what to expect, though it could mention potential rate limits or timeout considerations.
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 three sentences with no waste. The first sentence states the purpose and scope, the second clarifies premium behavior, and the third gives a usage recommendation. It is front-loaded and efficient.
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 no output schema and no parameters, the description adequately covers what the tool returns and important caveats (large size, premium stubs). It could be slightly more complete by mentioning format details (e.g., that content is in Markdown), but overall it is sufficient for an agent to use the tool correctly.
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 tool has no parameters, and the schema coverage is 100% (vacuously). The description adds value by explaining what the single output contains and how the response is structured, which is beyond the schema's empty definition.
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 returns the entire free corpus as one document, specifying exactly what is included (titles, descriptions, URLs, full Markdown bodies) and how premium resources are handled (stubs with purchase links). This distinguishes it from siblings like get_resource or list_resources, which have narrower scopes.
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 advises using this tool to 'ingest everything in a single call' and warns that 'the response is large', providing clear usage context. It does not explicitly state when not to use it, but the existence of siblings like get_resource implies alternatives for targeted access.
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 are clearly separated by resource type: articles, resources, corpus, stats, and articles. The main ambiguity is the access/pricing/payment cluster (`check_access`, `get_access_info`, `get_pricing`, `get_payment_info`), which agents could confuse despite distinct descriptions.
All tool names follow a consistent `verb_noun` pattern, using `get_*`, `list_*`, and `search_*` prefixes. The naming stays readable and predictable even with broader tools like `search` and `search_resources`.
14 tools is within the typical well-scoped range, and each major retrieval goal has a dedicated tool. It is slightly heavy because the access/pricing/payment information could arguably be consolidated, but it is not bloated.
The server covers discovery, listing, searching, fetching, corpus ingestion, editorial articles, stats, and access/pricing verification. As a read-only content/access server, full CRUD is not expected, though there is no direct category-based batch filter beyond a general search.