Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Text to knowledge graphs: topics, clusters, gaps, and research ideas via InfraNodus.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"textgraph": {
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add textgraph -- npx -y @reneturcios/textgraph mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"textgraph": {
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"textgraph": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过 GitHub 账户 CloudCorpRecords 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.0)于4天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js, Docker。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
textgraph turns a piece of text into a knowledge graph and answers questions about that graph: what the main topics and clusters are, which connections are missing, and what research questions or ideas those gaps suggest. It can also create a graph in an InfraNodus account, analyse an existing graph by name, and compare several texts through their graphs.
This package is a clean-room rebuild, and a labelled core subset: it covers 12 of the 22+ tools documented for the original InfraNodus MCP server. The omitted tools are listed under "Scope" below.
Every tool needs an InfraNodus API key:
export INFRANODUS_API_KEY="your key"
export INFRANODUS_API_URL="https://api.infranodus.com"
With no key set, every call fails with an error naming INFRANODUS_API_KEY, before any request is made.
Wire-format note: the original project's README documents the tools and their purposes, not the HTTP wire format. The endpoint shape used here is a documented reconstruction: each tool POSTs its JSON arguments to {INFRANODUS_API_URL}/ — the tool name with underscores, exactly as listed — with Authorization: Bearer INFRANODUS_API_KEY. Responses are parsed tolerantly: JSON is returned as structured text, a non-JSON body is returned as-is, and an upstream HTTP error is reported with its status and a body excerpt.
Local validation, applied before any request: analyze_text requires at least one of text or url; optimize_text_structure's responseType must be one of response, idea, question, or transcend; and overlap_between_texts / difference_between_texts require at least two texts.
npx @reneturcios/textgraph generate_content_gaps '{"text":"..."}'
Or run the container image:
docker run -i ghcr.io/cloudcorprecords/textgraph
The container defaults to the MCP server. Pass CLI arguments to use it as a CLI instead.
The CLI mirrors the tools: one subcommand per tool, taking its arguments as a single JSON object.
textgraph generate_knowledge_graph '{"text":"...","graphName":"notes"}'
textgraph analyze_existing_graph_by_name '{"graphName":"notes"}' textgraph analyze_text '{"url":"https://example.com/article"}' textgraph optimize_text_structure '{"text":"...","responseType":"idea"}' textgraph overlap_between_texts '{"texts":["first","second"]}' textgraph --version
Exit codes: 0 on success, 1 on an API or validation error (the error names the problem), 2 for usage errors.
One bin serves both faces. The mcp subcommand starts a stdio MCP server:
npx -y @reneturcios/textgraph mcp
MCP client configuration:
{
"mcpServers": { "textgraph": { "command": "npx", "args": ["-y", "@reneturcios/textgraph", "mcp"], "env": { "INFRANODUS_API_KEY": "your key" } } } }
| Tool | Input | What it does |
|---|---|---|
generate_knowledge_graph | text (required), graphName | Convert text into a knowledge graph: topics, concepts, relationships, clusters. |
analyze_existing_graph_by_name | graphName (required) | Retrieve and analyze an existing graph from the account. |
analyze_text | text and/or url (at least one required, validated locally) | Analyze a text or URL: topics, clusters, statements, graph structure. |
generate_content_gaps | text (required) | Detect missing connections and underexplored topics in a text. |
generate_topical_clusters | text (required), generateTopicalSummaries | Topics and keyword clusters from text. |
generate_contextual_hint | text (required) | Topical overview of a text to augment LLM responses. |
generate_research_questions | text (required) | Research questions bridging content gaps. |
generate_research_ideas | text (required) | Research ideas based on content gaps. |
optimize_text_structure | text (required), responseType (one of response / idea / question / transcend, validated locally) | Bias/coherence analysis. |
create_knowledge_graph | text (required), graphName, statements, categories | Create a graph in the account from text and return its link. |
overlap_between_texts | texts (array, at least 2, validated locally) | Similarities between texts via their graphs. |
difference_between_texts | texts (array, at least 2, validated locally) | What's in the other texts but not the first. |
Omitted from this core subset: optimize_reasoning, generate_responses_from_graph, develop_conceptual_bridges, develop_latent_topics, develop_text_tool, generate_ontology_graph, analyze_llm_results, merged_graph_from_texts, analyze_google_search_results, analyze_youtube_results, and any tools beyond the fetched README list. The summaries, ideas, and questions these tools return are produced by the upstream InfraNodus service; this package reproduces the tool surface and the request/response handling, not that service's analysis quality.
npm install
npm run build # tsc -> dist/ npm test # node test runner over test/*.test.ts, fake fetch only, no network npm run dev # run the CLI from source via tsx
MIT — see LICENSE.