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Context7 indexes library documentation and serves it to AI coding agents through an MCP server. Instead of relying on training data that may be outdated, your agent pulls live Coval docs on demand.

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Why Use Context7

AI coding agents often hallucinate API details or reference outdated patterns. Context7 solves this by fetching current documentation at query time:
  • Always current — pulls from the latest published docs, not stale training data
  • Code-first — returns relevant code snippets and examples, not walls of text
  • Zero config — works out of the box with any MCP-compatible agent

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Coval on Context7

Coval’s full documentation is indexed and available:

Coval on Context7

Browse the Coval library on Context7 — includes CLI commands, API examples, metric configuration, and more.

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Install the Context7 MCP Server

Add Context7 to your agent’s MCP configuration:

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Usage

Once installed, your agent has two tools available:

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1. Resolve Library ID

Find Coval’s library ID by searching for it:

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2. Query Documentation

Ask questions and get back relevant code snippets and docs:
Your agent calls these tools automatically when it needs Coval context. You can also prompt it explicitly:
“Use Context7 to look up how Coval metrics work”

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Example Workflow

Here’s what happens when your agent uses Context7 with Coval:
1

You ask a question

“How do I launch an evaluation run with the Coval CLI?”
2

Agent resolves the library

The agent calls resolve-library-id("Coval") and gets /llmstxt/coval_dev_llms_txt.
3

Agent queries the docs

It calls query-docs with your question and gets back current CLI examples and flags.
4

Agent responds with accurate info

You get a response grounded in the latest Coval documentation, not training data.

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Context7 vs Other Approaches

Context7 complements Skills and the Coval connector. Use Context7 when your agent needs to look something up. Use Skills when it needs to know how to evaluate well. Use the Coval connector when it needs to execute operations.