Skip to main content
Glama

Books Bestsellers

books_bestsellers
Read-onlyIdempotent

Get a current New York Times bestselling books list (e.g. hardcover-fiction, hardcover-nonfiction, combined-print-and-e-book-fiction). Returns rank, title, author, description, publisher, and weeks on list. Example: books_bestsellers({ list: "hardcover-fiction" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listNoBestseller list name, e.g. "hardcover-fiction", "hardcover-nonfiction", "combined-print-and-e-book-fiction". Default "hardcover-fiction".
_apiKeyNoOptional — your own NYT API key for higher limits; omit to use the shared Pipeworx key.

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, and openWorldHint, so the description does not need to cover those. It adds value by specifying the return fields (rank, title, author, description, publisher, weeks on list) and the default list name, which goes beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose, followed immediately by a concrete example. No unnecessary words; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description lists all key return fields. The tool is a simple read operation, so the description provides sufficient completeness for an agent to understand what it returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for both parameters. The description adds context by providing an example call and listing the default value for the 'list' parameter, and it clarifies that '_apiKey' is optional. This goes beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves a current New York Times bestselling books list, using specific verb 'Get' and resource 'bestselling books list'. It provides examples of list names, which distinguishes it from sibling tools that cover movies (movie_reviews) or other topics (top_stories).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does and gives list name options, but it does not explicitly state when to use this tool versus alternatives or provide when-not-to-use guidance. The context is clear enough for the intended use, but no exclusions or comparisons are offered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying sources with overlapping question-answering purposes. Entity-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also have fuzzy boundaries that make selection error-prone.

Naming Consistency2/5

Names mix conventions: verb_noun (list_subscriptions, search_articles, generate_llms_txt), bare verbs (remember, recall, forget), noun phrases (polymarket_arbitrage, pipeworx_trending, entity_profile), and an ask_* family with beta/grounded variants. There is no consistent verb or noun pattern across the set.

Tool Count2/5

With 35 tools, the surface is well above the 15-tool threshold for a focused server, and most tools are unrelated to the NYT domain implied by the server name. The breadth reflects a broad data-platform grab bag rather than a scoped, intentional tool set.

Completeness4/5

The query side is unusually complete: single-lookup, grounded lookup, deep research, claim validation, entity resolution, comparison, profile, change-feed, discovery, memory, and subscription lifecycle tools are all present. Minor gaps remain, such as no direct NYT article fetch by URL and no update path for stored memories, but agents can work around them.