Most Recommended Books
Server Details
Read-only MCP server for verified book recommendations and reading lists.
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Tool Definition Quality
Average 4.5/5 across 6 of 6 tools scored.
Each tool targets a distinct query: book recommenders, topic lists, person recommendations, series order, summaries, and catalog search. The descriptions clarify when to use which, with no overlap.
All tools use a consistent verb_noun snake_case pattern (get_book_recommenders, get_list, get_person_recommendations, get_series_reading_order, get_summary, search_books). The only exception is search_books using 'search' instead of 'get', but it still follows verb_noun.
With 6 tools, the set covers the core functionality of a book recommendation service without being too sparse or overwhelming. Each tool serves a clear purpose.
The tools cover major use cases: searching, lists, person recommendations, book recommenders, series order, and summaries. A potential minor gap is no dedicated tool for detailed single-book info beyond search results, but search_books provides that.
Available Tools
6 toolsget_book_recommendersWho recommends this book?ARead-onlyIdempotentInspect
Return every verified person who recommends a given book, each with their quote about it and a link to the original source. This is MRB's signature dataset — use it when a user asks 'who recommends Sapiens?', 'is Zero to One worth reading?', or wants social proof / expert opinions on a specific title. Accepts a title ('Sapiens') or author+title for disambiguation.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Book title, e.g. 'Sapiens' or 'Zero to One' |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description complements by stating the return includes 'verified person', 'quote', and 'link to original source'. This adds transparency about data quality and provenance beyond annotations. No contradictions.
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?
Two tightly written sentences that front-load the purpose and include usage examples. Every sentence adds value without redundancy. Highly 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 the tool has a single required parameter, full annotations, and an output schema (implied), the description provides sufficient context for an AI agent to decide when and how to use it. It covers the return structure, input options, and typical queries. Could include more on what 'verified' means or any limits, but overall robust for this simple tool.
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 includes a concise description for the title parameter. The description adds that you can use 'author+title for disambiguation', which is not reflected in the schema, providing helpful context. However, it doesn't specify how to pass the author, so it's incomplete. Baseline 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?
Description clearly states it returns verified recommenders with quotes and links for a given book. Distinguishes itself as 'MRB's signature dataset' and provides specific use cases like 'who recommends Sapiens?', which differentiates it from siblings like search_books or get_person_recommendations.
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 mentions when to use the tool (e.g., user asks 'who recommends Sapiens?', wants social proof). Specifies input format: title or author+title for disambiguation. While it doesn't list when not to use or direct alternatives, the sibling tool names provide context and the description implies its unique role.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_listGet a consensus best-books listARead-onlyIdempotentInspect
Return one of MRB's 1,300+ consensus 'best books' lists for a topic (startups, stoicism, finance, sci-fi…). Every book on a list was recommended by at least two independent expert sources and is ranked by how many sources mention it. Use this when a user asks for 'the best X books' and you want a ranking backed by multiple experts rather than one opinion. Accepts a topic name ('startup', 'personal finance') or MRB list slug.
| Name | Required | Description | Default |
|---|---|---|---|
| list | Yes | Topic or list slug, e.g. 'startup' or 'stoicism' | |
| limit | No | Max books to return (default 20) |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds valuable context about the multi-sourcing and ranking methodology beyond annotations, but does not contradict.
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?
Three sentences with no wasted words; front-loaded with main purpose; structured logically.
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?
Despite output schema not shown, description fully covers input, output nature (list of books with ranking), and methodology. Complete for agent understanding.
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% with descriptions for both 'list' and 'limit'. Description repeats that 'list' accepts topic or slug and mentions limit maximum, adding little beyond schema.
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?
Clearly states it returns a consensus best-books list from over 1300 lists, explains ranking methodology, and distinguishes from sibling tools like search_books and get_book_recommenders.
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 says 'Use this when a user asks for "the best X books" and you want a ranking backed by multiple experts rather than one opinion.' Provides clear guidance on when to invoke.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_person_recommendationsGet a person's book recommendationsARead-onlyIdempotentInspect
Return every book a specific person (entrepreneur, investor, author, celebrity…) has recommended, with their verified quote about each book and a link to the original source (interview, podcast, post). Use this when a user asks 'what books does Naval Ravikant recommend?', 'Tim Ferriss's favorite books', or wants reading habits of a specific public figure. Accepts a name ('Naval Ravikant') or MRB slug ('naval-ravikant').
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max books to return (default 20) | |
| person | Yes | Person's name or MRB slug, e.g. 'Naval Ravikant' |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| mode | Yes | |
| items | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. Description adds behavioral details: returns verified quotes and original source links, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action, no fluff. Each sentence adds value: definition then usage/parameters.
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 output schema exists and annotations are rich, description covers purpose, usage, and parameter guidance. No gaps identified.
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%. Description enhances 'person' parameter with usage examples (name or slug) and clarifies acceptable inputs beyond schema description.
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?
Clearly states it returns book recommendations for a person, including verified quotes and source links. Differentiates from sibling tools like 'get_book_recommenders'.
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?
Provides explicit examples of when to use ('Naval Ravikant recommendations', 'Tim Ferriss's favorite books'), but no explicit exclusion of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_series_reading_orderGet a series' reading orderARead-onlyIdempotentInspect
Return the complete publication-order book list for a book series — the order the author released them, including novellas and companion volumes where cataloged. Use this when a user asks 'what order do I read John Grisham?', 'Harry Potter books in order', or how many books a series has. Accepts a series or author name ('Jack Reacher', 'Brandon Sanderson').
| Name | Required | Description | Default |
|---|---|---|---|
| series | Yes | Series or author name, e.g. 'Jack Reacher' or 'John Grisham' |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds that it includes 'novellas and companion volumes where cataloged' and returns publication order, which are valuable behavioral details beyond annotations.
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 sentences: the first defines the action, the second gives usage context. It is front-loaded, concise, and every sentence adds value without redundancy.
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 an output schema and comprehensive annotations, the description covers purpose, usage, and behavioral traits fully. No gaps are apparent.
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% with a parameter description. The description adds context by repeating and emphasizing that it accepts both series and author names, providing extra clarity for agents.
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 complete publication-order book list for a book series', with specific verb and resource. It distinguishes from sibling tools like search_books and get_book_recommenders by focusing on reading order. Examples of user queries reinforce the purpose.
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 says 'Use this when a user asks...' and provides concrete examples. It does not include when-not-to-use or alternative tools, but the context is clear enough that agents can infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_summaryGet a book summaryARead-onlyIdempotentInspect
Return MRB's one-page summary of a book: the overview plus chapter-by-chapter notes (~435 titles available). Use this when a user asks 'summarize Atomic Habits', 'what is Sapiens about?', or wants the key ideas without reading the book. Set full_text=true only when the user explicitly wants the complete chapter notes — they can be very long.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Book title, e.g. 'Atomic Habits' | |
| full_text | No | Include full chapter contents (default false: overview + chapter titles) |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds contextual transparency about availability ('~435 titles available') and the variability of response length with full_text. No contradictions.
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?
Three sentences, each serving a distinct purpose: definition, usage scenarios, and parameter guidance. No wasted words, front-loaded with the core action.
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 the presence of an output schema, the description sufficiently covers what the tool does and its constraints. It could mention what happens when a title isn't found, but the availability note ('~435 titles') sets expectations. Overall completes the picture.
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%, but the description adds significant value: for 'title' it gives an example ('Atomic Habits'), and for 'full_text' it explains default behavior and warns about potential length. This goes beyond schema to aid correct usage.
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 MRB's one-page summary (overview plus chapter notes) and provides example queries users might ask, making the purpose immediately understandable. It distinguishes from siblings by specifying the exact type of summary returned.
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 tells when to use the tool ('when a user asks 'summarize Atomic Habits'...') and gives precise guidance for the full_text parameter ('only when the user explicitly wants the complete chapter notes'). It sets clear expectations about content length.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_booksSearch booksARead-onlyIdempotentInspect
Search Most Recommended Books' human-curated catalog by book title or author name. Returns ranked matches with author, publication year, how many verified people recommend each book, the top recommenders, a sourced quote, and a buy link. Use this when a user asks about a specific book, an author's books, or wants recommendations matching a title they half-remember. For 'who recommends X?' prefer get_book_recommenders; for 'what does person X recommend?' prefer get_person_recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 6) | |
| query | Yes | Book title or author name, e.g. 'Atomic Habits' or 'Cormac McCarthy' |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| mode | Yes | |
| items | Yes | |
| source | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by detailing the return format (ranked matches with author, year, recommenders, quote, buy link) and the nature of the search. No contradictions.
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 concise, front-loaded with the core purpose, and each sentence is informative. No verbose or redundant content. Sibling guidance is integrated efficiently.
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 the tool's simple search functionality with two parameters, strong annotations, and an output schema, the description covers all necessary aspects: what it does, what it returns, when to use it, and how it relates to siblings. No 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 coverage is 100% and both parameters have clear descriptions. The description echoes the schema for query but does not add nuance beyond 'book title or author name'. For limit, the schema mentions default 6 and description does not, so minimal additional value.
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 that the tool searches a human-curated book catalog by title or author name and returns ranked matches with specific fields. It distinguishes itself from sibling tools by explicitly naming alternatives.
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 provides explicit guidance on when to use this tool (specific book/author queries, half-remembered titles) and when to use alternatives (get_book_recommenders for 'who recommends X?', get_person_recommendations for 'what does person X recommend?').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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