ContextPull

Name: ContextPull
Author: mi2arun

by mi2arunversion 0.2.1i★ –iRuns on your computer

Registry nameio.github.mi2arun/contextpull

Install

Overview

Description from the maker (original English)

Exact document sections on demand for LLM agents: index in context, search, read, grep, neighbours.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "contextpull": {
      "command": "npx",
      "args": [
        "-y",
        "contextpull",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add contextpull -- npx -y contextpull serve

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": {
    "contextpull": {
      "command": "npx",
      "args": [
        "-y",
        "contextpull",
        "serve"
      ]
    }
  }
}

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": {
    "contextpull": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "contextpull",
        "serve"
      ]
    }
  }
}

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 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account mi2arun, 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 7231 servers in this category
Published to the registry more recently than 42% of the other 7231 servers in this category
Basic checks equal to or better than 100% of the other 7231 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 ContextPull 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 (0.2.1) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv), 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 3 days ago

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 44 days ago

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.

ContextPull

Website: https://mi2arun.github.io/contextpull/ · demo · docs · MCP Registry io.github.mi2arun/contextpull

Pull, don't push. ContextPull turns a folder of documents into something an LLM agent can pull from the way Claude Code pulls from a codebase: a small index that is always in context, and five tools that return exact sections on demand. The model never receives content it did not ask for.

Zero runtime dependencies. One SQLite file. Works offline.

ContextPull ships with a companion, ragbisect (formerly stagewise): a neutral benchmark harness that builds an eval set from your corpus and scores ContextPull next to bm25, dense and hybrid pipelines. Two names, one project, kept apart so the measurement stays independent of the thing it measures.

Status

M1 to M3 are built: store, ingest, the five operations, CLI, conformance suite, MCP server, Claude Code integration, LLM summaries, protocol client examples, ragbisect adapters, and a TypeScript reader and server. See the roadmap.

Measured

uv documentation, 603 sections, 221 self-generated questions, recall@5, from docs/testing.md:

config	recall@5	ms/q	model tokens/q
hybrid push (dense + bm25)	0.964	69	0
bm25 push	0.923	5	0
pull, Claude Code (10-question sample)	1.000	26,565	86,656
pull, gpt-5.4-mini with reasoning off	0.615	16,023	10,891

A strong agent reads the right section every time; a small no-reasoning model reads the right section when it reads (NDCG 0.96 given a hit) but misses the gold section on 38% of questions. Push retrieval is nearly free per query; pull costs tens of thousands of tokens. Both facts are in the table on purpose. The model's own searches surfaced the gold section on 88% of a sample, so most of the gap is snippets being answered from rather than read; a stricter prompt did not change that. An earlier version of this table showed 0.045 for the small model; that number came from a threading bug in the benchmark adapter and is retracted in docs/testing.md.

Try it

Fastest: ./scripts/demo.sh ingests the bundled fixture corpus and walks through index, search, read and grep, then prints the exact claude mcp add line. ./scripts/demo.sh ./your-docs does the same on your own folder.

uv tool install contextpull            # or: pip install contextpull   (PyPI: contextpull 0.2.0)

contextpull ingest ./docs # writes .contextpull/store.sqlite contextpull index # the always-in-context table of contents contextpull search "refund window" --in policy-2025.md contextpull read policy-2025.md#3 --context 1 contextpull grep TX-4419 contextpull neighbours specs.md#1 contextpull export-chunks > chunks.jsonl # ragbisect-compatible sections

In Claude Code

claude mcp add contextpull -- uvx --from "contextpull[mcp]" contextpull serve ./docs

The server ingests ./docs into ./docs/.contextpull/store.sqlite, puts the index into its instructions so it is always in context, and exposes the five tools. Ask a question; the trace shows search, then read, then an answer with [path.md#3] citations. contextpull claude-md prints a CLAUDE.md snippet if you want to tell the model about it explicitly. Add --summarizer openai:gpt-5.4-mini for model-written one-line summaries in the index (cached by document hash).

Any other MCP host works the same way; see examples/clients/ for TypeScript, Go and Java protocol clients and examples/direct_api_loop.py for using the tools straight from a model API with no server.

uv sync --extra pdf                                             # PDFs: headings inferred from font size

uv run contextpull ingest ./docs --embed-model openai:text-embedding-3-small # enables: search --mode hybrid uv run contextpull serve ./docs --transport http --port 8765 # streamable HTTP at /mcp for a shared read-only server

As a library

Embedding it in your own product, with access control and air-gap notes: docs/embedding.md and examples/embed_with_acl.py.

from contextpull import Store, Ops

with Store.open(".contextpull/store.sqlite") as store: ops = Ops(store) print(ops.index()) hits = ops.search("refund window", in_=["policy-2024.md", "policy-2025.md"]).hits for h in hits: print(h.id, h.heading_path, h.snippet) section = ops.read(hits[0].id).section

Tool definitions for any model API are in contextpull.tools.TOOLS (Anthropic shape) and openai_tools(); tools.json at the repo root is the same thing for other languages.

How it works

Ingest parses Markdown, text, docx, xlsx, pptx and (with the pdf extra) PDF into headings, paragraphs, tables and code, and cuts heading-aware sections with stable ids like policy-2025.md#3. Tables and code are never split mid-block; long tables are split by rows and every part carries the header. Unchanged files are skipped on re-ingest.
Store is one SQLite file with an FTS5 index whose tokenizer keeps identifiers whole (--no-cache, UV_CACHE_DIR, TX-4419, 3.12).
Index is a token-budgeted table of contents, one line per document, delivered into the model's context. It goes hierarchical when a corpus is too large for the budget.
Tools: index, search (ids and snippets, never bodies), read (verbatim), grep (exact matches), neighbours (the header row, the next clause).

Node

sdk/typescript/ is a store-native reader and MCP server in TypeScript over better-sqlite3: open the same store file, no Python at query time. Published to npm as contextpull.

npx contextpull serve /path/store.sqlite                   # npm: contextpull 0.1.0

claude mcp add contextpull -- npx -y contextpull serve /path/store.sqlite

It passes the same conformance suite as the Python reference and returns identical results over MCP. Ingest stays in Python (npx contextpull ingest delegates to uvx contextpull ingest).

Go

sdk/go/ is a store-native reader and a single static binary server, pure Go, no cgo: the shape for a shared read-only deployment.

cd sdk/go && go build -o contextpull-server ./cmd/contextpull-server

./contextpull-server serve /data/store.sqlite --http 0.0.0.0:8765 # or without --http for stdio

Same conformance suite, identical results to Python over MCP.

Other languages

The store file is the contract. docs/store-format.md says what a reader must do; conformance/ holds a fixture corpus, its store and expected results. An SDK in any language is done when check passes. See the SDK plan.

Docs

Start at docs/README.md: architecture, design, system design, tool reference, evaluation, roadmap, decision records.

Measured with ragbisect, which lives next door.

ContextPull

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

ContextPull

Status

Measured

Try it

In Claude Code

More

As a library

How it works

Node

Go

Other languages

Docs