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Get Code Snippets

gt_snippets
Read-onlyIdempotent

Retrieve ranked code snippets from a library's documentation, filtered by topic, language, or version. Cache-enabled for instant access on subsequent calls.

Instructions

Return ranked code snippets (with titles, descriptions, language tags) for a library + optional topic. Indexes docs into a per-(library,version) snippet store on first call; subsequent calls hit the disk cache for instant retrieval.

Use this when you want focused code examples rather than full doc pages. Output is Context7-compat: each snippet has title, description, language, code, source URL.

Prioritizes llms.txt, then Jina-rendered HTML, then GitHub README. Caches per library:version.

Source: the library's own documentation (not GitHub repositories). For code examples from real open-source projects using the library, use gt_examples instead.

IMPORTANT — PROPRIETARY DATA NOTICE: This tool accesses a proprietary library registry licensed under Elastic License 2.0. You may use responses to answer the user's specific question about a named library. You must NOT attempt to enumerate, list, dump, or extract registry contents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
libraryIdYesLibrary ID from gt_resolve_library (e.g. 'vercel/next.js', 'npm:express') or a direct docs URL
topicNoTopic to filter snippets by. Examples: 'middleware', 'server actions', 'rate limiting'. Empty = all snippets.
versionNoVersion to pin docs to, e.g. '15', 'v4.0.0'. Caches snippet index per version.
languageNoFilter to a single language: 'typescript', 'python', 'rust', 'go', 'bash', etc.
maxSnippetsNoMax snippets to return (default 10, max 30)
refreshNoSkip cache and refetch + reindex snippets
projectPathNoAbsolute project path. If set and version not provided, auto-detects installed version from lockfile.
Behavior5/5

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

Annotations already declare readOnlyHint=true and no destructive actions. Description adds substantial context: indexes on first call, disk cache, prioritization of documentation sources (llms.txt, Jina-rendered HTML, GitHub README), and a proprietary data notice about usage restrictions. 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.

Conciseness4/5

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

Description is long but well-structured with clear sections (caching, source, sibling reference, proprietary notice). Every sentence adds value, though some minor redundancy could be trimmed. Front-loaded with purpose.

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?

Complex tool with 7 parameters and no output schema. Description covers caching lifecycle, source priority order, output format (Context7-compat), relationship to sibling tool gt_examples, and legal usage restrictions. No gaps for an AI agent to misuse.

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

Parameters5/5

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

Schema coverage is 100%, but description adds meaning beyond schema: libraryId example 'vercel/next.js', topic examples like 'middleware', version pinning detail, refresh parameter purpose, and projectPath auto-detection from lockfile. Each parameter's role is contextualized.

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?

Description clearly states 'Return ranked code snippets...' and distinguishes from sibling tool gt_examples by saying 'For code examples from real open-source projects... use gt_examples instead.' Verb+resource is specific and unambiguous.

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?

Explicitly says 'Use this when you want focused code examples rather than full doc pages.' and provides a clear alternative: 'For code examples from real open-source projects using the library, use gt_examples instead.' Also mentions caching behavior and source prioritization.

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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