Prototype · Preview: every server in the official registry, synced every 30 minutes. Reviews and accounts come in a later step.
PopNx MCPMCP servers for people who use AI · explained in plain words

MarkIndex

by jackjin1997version 0.1.1i★ –iRuns on your computer
Registry nameio.github.jackjin1997/markindex

Install

Overview

Description from the maker (original English)

Vectorless, structure-first search over large Markdown libraries: section trees + zh/en BM25

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "markindex": {
      "command": "npx",
      "args": [
        "-y",
        "markindex",
        "mcp",
        "--reindex-on-start"
      ]
    }
  }
}

Run this in your terminal

claude mcp add markindex -- npx -y markindex mcp --reindex-on-start

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "markindex": {
      "command": "npx",
      "args": [
        "-y",
        "markindex",
        "mcp",
        "--reindex-on-start"
      ]
    }
  }
}

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.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "markindex": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "markindex",
        "mcp",
        "--reindex-on-start"
      ]
    }
  }
}

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.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account jackjin1997, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7249 servers in this category
  • Published to the registry more recently than 88% of the other 7249 servers in this category
  • Basic checks equal to or better than 100% of the other 7249 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MarkIndex safe?

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.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

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).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

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.

MarkIndex

Vectorless, structure-first retrieval over large libraries of long Markdown documents — for agents.

MarkIndex parses every Markdown file into a tree of its own sections, stores the trees, the original lines and a mixed Chinese/English BM25 index in one SQLite file, and gives an agent five read-only tools to walk "library → folder → document → section" and read the original text with line numbers. No embeddings, no vector database, no chunking guesswork: the document's own structure is the index, and every piece of evidence can be cited back as [doc_path, L - ].

Inspired by VectifyAI/PageIndex, MarkIndex is an independent,

unofficial TypeScript implementation focused on Markdown. It is not affiliated with or endorsed by VectifyAI.

中文说明:README.zh-CN.md

Why

  • Structure-first, not chunk-first. Long professional documents (filings, guidelines, laws, manuals) are already organized by their authors. Navigating that outline keeps context intact and makes answers explainable and citable; arbitrary chunks + vector similarity cut sections apart.
  • Deterministic and LLM-free indexing. Parsing (mdast, not regex), tree building and the token-cost tree optimization never call a model; the same input always produces the same tree. LLMs are optional and only used for node summaries, document descriptions and the built-in retrieve() loop.
  • Markdown done properly. Setext headings, preface text before the first heading, documents without any heading, front matter, # inside code fences, skipped heading levels, BOM / CRLF — every source line belongs to exactly one node, and oversized sections are split at block boundaries into chunk nodes.
  • Keyword fallback that works for Chinese. BM25 over Intl.Segmenter word segmentation (Chinese, English and mixed text, identifiers like C++, ISO-27001, v2.1), plus a trigram substring index as a second pass.
  • Budgeted tool responses. Tools never throw; they return JSON envelopes with next_steps guidance and stay under a 95 000-character budget, paginating large outlines and sections.

Install

npm install markindex          # library + CLI (Node.js >= 22.12)

npx markindex --help

From source:

git clone https://github.com/jackjin1997/markindex.git && cd markindex

npm install && npm run build node dist/cli.js --help # or npm link to get a markindex command

Optional, only for LLM features (summaries, retrieve(), agent-mode evaluation): install the AI SDK provider you want, e.g. npm install @ai-sdk/anthropic, and set its API key (ANTHROPIC_API_KEY, …). With npx and no local install, put the provider in the same temporary environment:

npx -p markindex -p @ai-sdk/anthropic markindex index ./docs --summary-model anthropic:claude-sonnet-5-5

CLI

# Build or incrementally update the index (stored in <root>/.markindex/index.db)

markindex index ./docs markindex index ./docs --summary-model anthropic:claude-sonnet-5-5 --estimate # cost estimate, no LLM calls markindex index ./docs --summary-model anthropic:claude-sonnet-5-5 # with node summaries markindex index ./docs --summary-model anthropic:claude-sonnet-5-5 --summary-rpm 50 --summary-tpm 40000 # rate-limited markindex index ./docs --json # machine-readable report markindex index ./docs --no-setext # converter output with --- page breaks markindex index ./docs --ignore 'drafts/' --ignore '*.tmp.md' # extra ignore patterns (repeatable)

Inspect one document's section tree

markindex tree ./docs guides/setup.md --depth 3

Keyword search (BM25 + trigram fallback)

markindex search ./docs "离职 补偿" markindex search ./docs "ISO-27001 audit" --folder policies --limit 5 --json

Serve the library to agents over MCP (stdio, read-only)

markindex mcp ./docs markindex mcp ./docs --reindex-on-start

Measure retrieval quality on your own documents (see "Evaluation")

markindex eval ./docs --questions questions.jsonl --mode bm25 --out report.json markindex eval ./docs --questions questions.jsonl --mode agent --model anthropic:claude-sonnet-5-5 --out report.json markindex eval ./docs --questions questions.jsonl --mode agent --model anthropic:claude-sonnet-5-5 --rpm 50 --tpm 40000

Rate limits. Calls are always bounded by concurrency (--concurrency for eval; 8 for summaries) and the AI SDK's retries, which already honor retry-after / retry-after-ms on 429 / 5xx responses (up to 60 s). To stay under a provider quota, add a client-side limit: --summary-rpm / --summary-tpm on index, --rpm / --tpm on eval (agent mode). rpm caps requests and tpm caps *estimated input* tokens in any sliding 60 s window; both are shared by all documents (index) or all questions (eval) of the run, and waiting callers are served in order. Input tokens are estimated from the prompt text (about 1 token per CJK character, otherwise 4 characters), so leave headroom below the real quota; output tokens and tool definitions are not counted. A single request larger than the tpm still goes through, alone. In the library, pass rateLimit: { requestsPerMinute, inputTokensPerMinute } to summarizeDocument / indexLibrary({ summary }) or retrieve(); reuse the same object to share one budget.

--summary-model / --model take : ; MarkIndex imports @ai-sdk/ dynamically, so only the provider you use needs to be installed. Every command accepts --db to use a different index file. Put the index on a local disk: the writer lock uses hard links and SQLite runs in WAL mode, neither of which works reliably on exFAT, SMB / NFS shares or other network volumes. If the documents live there, keep them in place and point --db at a local path (e.g. --db ~/.cache/markindex/docs.db).

Indexing is incremental: files are compared by content hash, only new or changed documents are rebuilt (one transaction per document), deleted files are removed, and summaries are cached by node content hash. Scanned files are .md and .markdown (not .mdx). Ignored are: paths matched by the library's own .gitignore files (at any level under the root; global git excludes and .git/info/exclude are not read), by an optional .markindexignore file at the root (same syntax, for rules you don't want in .gitignore) and by --ignore options (gitignore syntax, relative to the root, repeatable; ignore: string[] in the library API; also accepted by mcp --reindex-on-start; a leading ./ is dropped, a leading / anchors at the root as in gitignore, and absolute filesystem paths or ../ are rejected). The sources only add exclusions: a ! rule in .markindexignore or --ignore cannot re-include a path that a .gitignore excludes. A document that becomes ignored is removed from the index on the next run. Dot-directories such as .git and .markindex are skipped and symlinks are not followed. One writer at a time (a second index fails fast); readers such as the MCP server are never blocked. When the index was built by an older MarkIndex version or with different tree parameters, the next index run rebuilds every document (full rebuild: … in the output, rebuilt in the report); cached summaries are reused.

Page breaks from PDF / HTML converters. Converters often emit a bare --- line right after a page's last paragraph. CommonMark reads that as a setext heading, so the whole paragraph becomes a level-2 heading. index warns when it sees setext h2 headings whose text spans several lines or exceeds 120 characters (warning : … on stdout, warnings in the JSON report). For such libraries pass --no-setext (also on mcp --reindex-on-start; setext: false in the library API): --- then is a thematic break and === plain text. The setting is stored in the index: later runs without the flag (index, mcp --reindex-on-start, Library.index()) keep it; pass --setext (setext: true) to turn it back on. Changing it rebuilds the index.

Library usage

import { openLibrary } from 'markindex'

// Index (or update) a folder, then open it read-only for agents. const writer = openLibrary({ root: './docs', readonly: false }) await writer.index() // { added, updated, removed, unchanged, skipped, summaryErrors, warnings } writer.close()

const lib = openLibrary({ root: './docs' }) const envelope = await lib.callTool('search_documents', { query: 'termination notice period' }) console.log(JSON.parse(envelope)) // { success: true, data: { results: [...] }, next_steps: {...} }

With the Vercel AI SDK (your agent drives the tools)
import { generateText, stepCountIs } from 'ai'

import { anthropic } from '@ai-sdk/anthropic' import { AGENT_INSTRUCTIONS, createAiSdkTools, openLibrary } from 'markindex'

const lib = openLibrary({ root: './docs' }) const { text } = await generateText({ model: anthropic('claude-sonnet-5-5'), system: AGENT_INSTRUCTIONS, // browse first, read structure, cite [doc_path, L - ] tools: createAiSdkTools(lib), stopWhen: stepCountIs(15), prompt: 'What notice period applies when an employee resigns?', })

Built-in evidence retrieval

retrieve() runs the same tools in a model loop and returns evidence only (original text re-read from the index by line range — never model-written text), plus usage and why it stopped:

import { anthropic } from '@ai-sdk/anthropic'

import { openLibrary, retrieve } from 'markindex'

const lib = openLibrary({ root: './docs' }) const { evidence, usage, stopReason } = await retrieve(lib, 'What notice period applies when an employee resigns?', { model: anthropic('claude-sonnet-5-5'), maxSteps: 12, maxInputTokens: 200_000, rateLimit: { requestsPerMinute: 50, inputTokensPerMinute: 40_000 }, // optional, client-side }) for (const e of evidence) console.log(${e.docPath} L${e.startLine}-${e.endLine}, e.titlePath.join(' > '))

Lower-level building blocks are exported too: parseMarkdown, buildTree, optimizeTree, openStore, indexLibrary, searchNodes, summarizeDocument, estimateSummaryCost, TOOL_CONTRACT, createMcpServer.

MCP setup

Build the index first (npx markindex index /path/to/docs), then register the server. MarkIndex is also listed in the official MCP Registry as io.github.jackjin1997/markindex, so registry-aware clients can install it from there (they start it as npx markindex mcp --reindex-on-start).

Claude Code

claude mcp add markindex -- npx markindex mcp /path/to/docs

Cursor (~/.cursor/mcp.json or .cursor/mcp.json in a project)

{

"mcpServers": { "markindex": { "command": "npx", "args": ["markindex", "mcp", "/path/to/docs"] } } }

The server sends AGENT_INSTRUCTIONS as its MCP instructions and marks all tools read-only.

Tools

ToolWhat it does
browse_documentsList a folder: sub-folders and documents (path, title, description, tokens), paginated. The default discovery tool.
get_documentOne document's metadata, front matter, size and read_structure_first (true above 15 000 tokens).

| get_document_structure | The section outline (node ids, titles, line ranges, tokens, summaries

Only the beginning is shown; read the rest in the repo

Ask PopNx