Get a free sample (typically first chapter) of a book. No purchase required. Use this when the user wants to evaluate a book's content before deciding to buy, or asks what a specific book is like / what's in it.
| Name | Required | Description | Default |
|---|---|---|---|
| book_id | Yes | The book ID |
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?
With no annotations, the description carries the full burden. It discloses key behavioral aspects: it's free, no purchase required, and provides a sample. But it omits details on output format, variability (e.g., if no preview exists), and any access requirements, leaving some behavioral ambiguity.
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 two concise sentences, front-loaded with the core action, and every sentence adds value. No filler or repetition.
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 simple one-parameter tool with no output schema, the description is largely complete: it covers what, when, and the free aspect. It omits return format details, but these are not critical for an agent to invoke the tool correctly, making it complete enough.
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 provides 100% coverage for book_id, and the description adds no extra parameter-specific meaning. Since the schema already documents the parameter minimally, a baseline score of 3 is appropriate.
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 identifies the tool as retrieving a free sample (typically first chapter) of a book, with a specific verb and resource. It distinguishes itself from sibling tools like read_chapter or download_book by emphasizing the free, preview nature.
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?
Explicitly states when to use: to evaluate a book's content before buying or to answer what a book is like. However, it does not mention alternative tools or when-not-to-use scenarios, so it falls short of the full 5.
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.
Several tools have overlapping purposes. For example, list_my_books and my_books both list authored books, and author_dashboard is a superset; list_orders and my_orders are near-duplicates; check_earnings and payout_balance both report earnings. This creates confusion and risks misselection.
Naming is inconsistent. While many tools use verb_noun (list_annotations, get_book_details), others use my_* (my_books, my_orders), bare nouns (payout_balance), or compound verbs (connect_onboard). The lack of a uniform pattern makes the set feel disjointed.
49 tools is excessive for a server, especially with at least three sets of near-duplicates. The platform is complex, but many tools could be merged (e.g., my_books and list_my_books, list_orders and my_orders). This count will overwhelm agents and increase misselection risk.
The toolset covers the core domain well: discover, purchase, read, comment, review, annotate, publish, monetize, verify, and manage payouts. Minor gaps exist, such as no update/delete annotation, no delete review, and no single-item getter for annotations/reviews, but these are not critical to the main workflows.