Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Text to knowledge graphs: topics, clusters, gaps, and research ideas via InfraNodus.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"textgraph": {
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add textgraph -- npx -y @reneturcios/textgraph mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"textgraph": {
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"textgraph": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@reneturcios/textgraph",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account CloudCorpRecords, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 4 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, Docker on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.