Skip to main content
Glama

Get Chapter

get_chapter

Retrieve specific chapter content by title or index for efficient document access. Ideal for targeted reading, updating chapters, or navigating large files without loading the entire document. Includes summary and navigation details.

Instructions

Retrieve a single chapter's content by title or index.

When to use this tool:

  • Reading specific chapter content

  • Reviewing targeted information

  • Updating specific chapter (read first)

  • Accessing chapter without loading entire document

  • Efficient partial document access

Key features:

  • Access by title OR index (0-based)

  • Returns navigation info (has_next, has_previous)

  • Memory-efficient for large documents

  • Includes chapter summary

You should:

  1. Use chapter_title for known chapters

  2. Use chapter_index for sequential reading

  3. Specify either title OR index, not both

  4. Use exact title match (case-sensitive)

  5. Consider using get_next_chapter for sequences

  6. Cache results if accessing multiple times

DO NOT use when:

  • Need multiple chapters (batch operations)

  • Don't know chapter title or index

  • Need full document context

Returns: {success: bool, title: str, content: str, summary: str, index: int, total_chapters: int, has_next: bool, has_previous: bool}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chapter_indexNoZero-based index of the chapter (use this OR chapter_title)
chapter_titleNoTitle of the chapter to retrieve (use this OR chapter_index)
filenameYesKnowledge file name (must include .md extension)
project_idYesThe project identifier
Behavior4/5

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

With no annotations provided, the description carries full burden and delivers substantial behavioral context. It discloses key traits: memory efficiency for large documents, case-sensitive exact title matching, navigation info in returns, and caching recommendations. It doesn't mention error handling or performance characteristics, keeping it from a perfect score.

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?

The description is well-structured with clear sections (purpose, when to use, key features, instructions, exclusions, returns) and every sentence earns its place. It's comprehensive yet avoids redundancy, with the most critical information (purpose and basic usage) appearing first.

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?

For a read operation with no annotations but 100% schema coverage, this description provides excellent contextual completeness. It explains when to use the tool, behavioral characteristics, parameter usage rules, sibling relationships, and detailed return structure (even without an output schema), leaving minimal gaps for an AI agent.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds some value by explaining the title/index mutual exclusivity ('Specify either title OR index, not both') and providing usage guidance ('Use chapter_title for known chapters'), but doesn't add significant semantic details beyond what the schema provides.

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 the tool's purpose with specific verbs ('Retrieve a single chapter's content') and resources ('by title or index'), distinguishing it from siblings like list_chapters (which lists multiple chapters) and get_knowledge_file (which retrieves entire documents). The opening sentence provides precise, actionable intent.

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

Usage Guidelines5/5

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

The description includes explicit 'When to use this tool' and 'DO NOT use when' sections, providing clear positive and negative guidance. It names alternatives (get_next_chapter for sequences) and specifies prerequisites (knowing chapter title or index), making it highly actionable for an AI agent.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sven-borkert/knowledge-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server