ChatGPT Plugin

Context7

Fetch Up-to-date Docs

Brand: Context7

Category: Developer Tools
Primary Subcategory: Developer Docs & Reference Search

Integration details

Description

LLMs rely on outdated or generic information about the libraries you use. You get outdated code examples based on year-old training data and hallucinated APIs that don't even exist. Context7 fetches up-to-date code examples and documentation right into your LLM's context. No tab-switching, no hallucinated APIs that don't exist, no outdated code generation.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Developer Tools
Primary Subcategory: Developer Docs & Reference Search
Secondary Subcategories: None listed
Brand: Context7
Access: Account required
First tracked: 2026-04-27
Tool count: 2
Market: US

Agent DiscoverabilityChatGPT Plugin Discovery Score

ChatGPT organic discovery is live

Your Plugin is discoverable

ChatGPT now suggests Plugins on its own when they match a user's request.Context7 is being suggested, and your Discoverability Score is being created. It will show here as soon as it’s ready.

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Primary Subcategory

Competing in ChatGPT Developer Docs & Reference Search

View Category

Listed in alphabetical order

Primary Subcategory

@imqueueComing soonGet notified

3GPP ScoutComing soonGet notified

AmtstoolsComing soonGet notified

ArezComing soonGet notified

Autodesk Product HelpComing soonGet notified

Context7Coming soonGet notified

Tools

2 tools agents can invoke

Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework. You must call 'Resolve Context7 Library ID' tool first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Do not call this tool more than 3 times per question.
query-docs
Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'Query Documentation' tool to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Each result includes: - Library ID: Context7-compatible identifier (format: /org/project) - Name: Library or package name - Description: Short summary - Code Snippets: Number of available code examples - Source Reputation: Authority indicator (High, Medium, Low, or Unknown) - Benchmark Score: Quality indicator (100 is the highest score) - Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version. For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.
resolve-library-id

FAQs

Context7 ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve Context7's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Context7 alternatives on ChatGPT?

As of 2026-11-03, Context7 competes with 3GPP Scout, @imqueue, Amtstools, Arez, Autodesk Product Help, CliDeck Network Knowledge, Cyber Library, Deriv and 20 more in ChatGPT Developer Docs & Reference Search, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.