contextweaver

Name: contextweaver
Author: dgenio

by dgenioversion 0.18.2i★ –iRuns on your computer

Registry nameio.github.dgenio/contextweaver

Install

Overview

Description from the maker (original English)

MCP gateway: bounded tool shortlists + artifact-backed result firewall. Deterministic, no model.

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: Other

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

PyPIPython’s uv installed (for the uvx 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": {
    "contextweaver": {
      "command": "uvx",
      "args": [
        "contextweaver",
        "mcp",
        "serve",
        "--config",
        "your-value"
      ]
    }
  }
}

Run this in your terminal

claude mcp add contextweaver -- uvx contextweaver mcp serve --config your-value

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": {
    "contextweaver": {
      "command": "uvx",
      "args": [
        "contextweaver",
        "mcp",
        "serve",
        "--config",
        "your-value"
      ]
    }
  }
}

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": {
    "contextweaver": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "contextweaver",
        "mcp",
        "serve",
        "--config",
        "your-value"
      ]
    }
  }
}

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 dgenio, 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 Otheri

More stars than 0% of the other 32435 servers in this category
Published to the registry more recently than 31% of the other 32435 servers in this category
Basic checks equal to or better than 100% of the other 32435 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 contextweaver 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 (0.18.2) 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) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Apify MCP Server

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

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday

Install

Apify MCP Server

by apify

Extract data from any website with thousands of scrapers, crawlers, and automations on Apify Store ⚡

Runs in the cloudUpdated today

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.

contextweaver

Capture an agent's effective capability surface, commit it, and see semantically meaningful changes before deployment.

ContextWeaver is currently testing a deliberately narrow product hypothesis: capability snapshot + semantic drift.

Given an OpenAPI document, a captured MCP tools/list response, or a native ContextWeaver catalog, the D1 experiment produces a deterministic normalized snapshot that you can inspect, verify, and compare with a later candidate. It does not require a model account, a gateway, a tool executor, or the Weaver Stack.

Status: alpha, and specifically a product experiment. The implementation works and is tested; the user-value hypothesis is not yet proven. The project is actively measuring whether independent users keep this workflow after trying it on real projects.

Try the capability-drift experiment

Clone the repository and install that checkout so the maintained example fixtures and the code under evaluation are guaranteed to match:

Commands below use python3, matching the repository's documented default (docs/agent-context/workflows.md). On Windows installations that expose Python through the launcher, replace python3 with py -3; if your environment already exposes the intended interpreter as python, use that consistently.

git clone --depth 1 https://github.com/dgenio/contextweaver.git

cd contextweaver python3 -m pip install .

Run the maintained OpenAPI example:

python3 -m contextweaver.d1 snapshot examples/d1/openapi_before.json --source-type openapi --output ./cw-before.json

python3 -m contextweaver.d1 snapshot examples/d1/openapi_after.json --source-type openapi --output ./cw-after.json python3 -m contextweaver.d1 inspect ./cw-after.json python3 -m contextweaver.d1 verify ./cw-after.json python3 -m contextweaver.d1 diff ./cw-before.json ./cw-after.json

The candidate fixture intentionally:

makes customer_id required on the existing listInvoices capability;
changes its description;
adds a new getInvoice capability.

The diff separates capability additions/removals from changes to an existing logical capability and reports the structured paths that changed. Contract changes are separated from documentation-only changes. Changes involving fields such as required, type, or enum are flagged as potentially breaking for review.

That flag is intentionally conservative: ContextWeaver does not claim to be a complete JSON-Schema compatibility checker.

Full walkthrough: Capability drift experiment.

Use it on your own source

OpenAPI

python3 -m contextweaver.d1 snapshot ./openapi.yaml --source-type openapi --output ./capabilities.json

python3 -m contextweaver.d1 verify ./capabilities.json

After the API changes:

python3 -m contextweaver.d1 snapshot ./openapi.yaml --source-type openapi --output ./capabilities-candidate.json

python3 -m contextweaver.d1 diff ./capabilities.json ./capabilities-candidate.json

Captured MCP tools

If you already have an MCP tools/list response saved as JSON:

python3 -m contextweaver.d1 snapshot ./tools-list.json \

--source-type mcp \ --output ./capabilities.json

For MCP, D1 compares tools by their upstream logical name so an input-schema edit appears as a change to the same capability rather than an unexplained remove/add pair. The historical schema-sensitive routing ID is retained separately as normalized_id for inspection.

Capturing a live MCP server is a separate operation. snapshot, inspect, diff, and verify do not execute discovered capabilities.

Native ContextWeaver catalog

python3 -m contextweaver.d1 snapshot ./catalog.json \

--source-type native \ --output ./capabilities.json

What `verify` means

verify checks the D1 snapshot contract: structure, deterministic ordering, logical-ID uniqueness, and the canonical capability digest.

It is not:

deployment approval;
security certification;
authentication or authorization;
a guarantee that a tool implementation is correct;
routing-quality evaluation;
production runtime attestation.

When not to use ContextWeaver D1

A negative answer is useful evidence for this project. Do not add ContextWeaver just because capability snapshots sound tidy.

Use something simpler when:

ordinary Git diff, config review, and tests already make your capability changes obvious;
your tool/API surface is tiny and rarely changes;
provider-native tool search is the only problem you are trying to solve;
you need an agent loop, tool executor, IAM layer, or production orchestrator;
maintaining another committed artifact costs more than the review/debugging problem it removes.

If you try D1 and conclude that Git/tests are cheaper, that is a valid product result — please say so.

What is being tested

The current survival experiment asks a stronger question than whether the code works:

Do capability snapshots and semantic drift reports improve a real

review/manual/risk process enough that independent users keep them?

The project distinguishes:

qualified exposure

-> understood the problem -> chose to evaluate -> attempted setup -> reached first useful output -> used on a real project -> retained independently / removed

Stars, forks, downloads, a successful demo, and maintainer-created integrations are not treated as retained adoption.

The controlling product decision is tracked in #758, and the distribution quality gate is #855. Unassisted first success and retention are tracked in #658 and genuine adoption in #551.

What about routing, context compilation, and the MCP gateway?

ContextWeaver already contains substantial historical runtime functionality. That code still exists and currently shipped behavior should remain truthful and safe, but existing implementation is not evidence that the project should keep expanding it.

Two broader hypotheses are explicitly evidence-first:

D2 — bounded / phase-aware context compilation: conditional. It must show consequential value beyond contemporary provider/runtime-native mechanisms.
D3 — custom deterministic tool selection: a falsification track. It must beat modern provider-native tool search/deferred loading or a simple retrieval baseline on something target users actually care about.

During the D1 experiment, the project is not expanding routing sophistication, runtime bundle machinery, memory/session surfaces, framework breadth, gateway scope, vector stores, or model-assisted enrichment without a concrete external blocker or approved falsification experiment.

If you are maintaining an existing integration that uses those historical surfaces, the relevant documentation remains available:

Which historical pattern fits?
Context firewall
MCP Context Gateway architecture
MCP gateway security model
Comparison / alternatives
Ecosystem map

Evidence and claims

The D1 implementation supports scoped engineering claims such as deterministic snapshot construction under the documented source/adapter contract and structured semantic-diff output. It does not yet support the stronger claim that users need or retain the product.

The historical token-reduction headline is intentionally not used to sell D1. The current evidence-integrity work for those older benchmark claims is tracked in #841.

See Claims & evidence for the claim registry and Capability drift experiment for the exact D1 contract and limitations.

Python API stability

D1 is intentionally exposed through:

python3 -m contextweaver.d1 ...

rather than being promoted immediately into the historical top-level CLI or a large new public Python API. That is deliberate. The experiment should earn a permanent surface through real retained use before the project takes on another compatibility obligation.

Part of the Weaver Stack — optionally

ContextWeaver can be used standalone. It has no hard dependency on the sibling Weaver projects.

The wider Weaver Stack contains adjacent experiments/components for planning, execution boundaries, guardrails, lessons, and evaluation. That ecosystem is not required to evaluate D1, and Stack coherence is not a reason to preserve a ContextWeaver feature that does not justify itself independently.

See the Ecosystem map only if you actually need those adjacent responsibilities.

Install and compatibility

pip install contextweaver

Python 3.10–3.14 are covered by the repository CI matrix.

Current package version: 0.18.2

Project	Release
ContextWeaver (this repo, v0.18.2)	current package release

The repository is pre-1.0. Prefer the latest supported patch release for bug and security fixes, and check the changelog before relying on historical runtime APIs.

Current roadmap

The roadmap is intentionally a product-decision sequence, not a feature queue.

Milestone	Status	Meaning
v0.18.1 — D1 survival experiment baseline	✅ complete	Offline snapshot/inspect/diff/verify exists; user value remains unverified.
v0.18.2	✅ current (v0.18.2)	D1 survival experiment and release-path recovery
D1 distribution gate	🔬 evidence first	Make the front door understandable, recruit qualified evaluators, measure first success and retention.
D1 decision	⏸ next decision	Continue, shrink further, or kill based on retained value after competent distribution.
D2 / D3	🧪 conditional	Run only if D1 evidence or independent problem discovery justifies bounded falsification experiments.

A green CI run does not advance this roadmap by itself.

Contributing

The most valuable contributions during the survival experiment are narrow and evidence-linked:

a real D1 evaluator blocker;
a semantic-diff case that is currently misleading or silently lost;
deterministic normalization correctness;
security/release maintenance for behavior the package still ships;
negative evidence showing a simpler alternative wins.

Please do not add a framework adapter, routing policy, storage backend, runtime phase, or ecosystem integration solely for completeness.

See CONTRIBUTING.md and AGENTS.md for repository engineering conventions.

Security

See SECURITY.md for supported-ve

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

contextweaver

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 Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Apify MCP Server

README from the maker

contextweaver

Try the capability-drift experiment

Use it on your own source

OpenAPI

Captured MCP tools

Native ContextWeaver catalog

What verify means

When not to use ContextWeaver D1

What is being tested

What about routing, context compilation, and the MCP gateway?

Evidence and claims

Python API stability

Part of the Weaver Stack — optionally

Install and compatibility

Current roadmap

Contributing

Security

What `verify` means