arsenkin-mcp
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# Arsenkin MCP
**Run your SEO checks in batches from your AI assistant.**
Collect SERPs, check keyword frequency, cluster queries or inspect indexation through Arsenkin Tools. The MCP server gives your assistant a workflow for estimating requests, getting approval, tracking jobs and working with saved results.
[Quick start](#quick-start) · [Connect your assistant](#connect-your-assistant) · [Issues](https://github.com/zai-one/arsenkin-mcp/issues)
Try asking your assistant:
> Prepare a frequency-check batch for my keyword list. Show the request plan and estimated cost before submitting anything.
## What you can do
| Your task | What the MCP server provides |
|---|---|
| Research keywords | Keyword frequency, related phrases, demand trends, search suggestions and clustering. |
| Inspect search visibility | SERP, position, indexation and relevant-URL checks. |
| Manage a batch | Discover profiles, estimate, approve, submit, diagnose the worker and export saved results. |
| Read the result | URL/host occurrence tables and cluster records with source pointers, in JSON or CSV. |
[Saved-result examples and worker help](docs/RESULT_TABLES.md) show how to turn a completed SEO task into a useful table.
## Quick start
Prefer a ready package? [Install the release and generate your client configuration](INSTALL.md#install-a-release-package). No source checkout is required.
Install **Python 3.12–3.14** and [uv](https://docs.astral.sh/uv/getting-started/installation/). Clone with Git or [download the ZIP](https://github.com/zai-one/arsenkin-mcp/archive/refs/heads/main.zip). With a ZIP, open the extracted directory and skip the first two commands.
Have your Arsenkin API token ready for the configuration wizard. Start with a local request estimate, as shown below; enable paid execution after reviewing the access and limits section.
```sh
git clone https://github.com/zai-one/arsenkin-mcp.git
cd arsenkin-mcp
uv sync --frozen --extra standalone
uv run --frozen --extra standalone python scripts/configure.py
uv run --frozen --extra standalone arsenkin-mcp --config mcp.local.json --check-config
```
The wizard creates a local configuration and stores secrets in private files. It refuses to overwrite an existing setup. `--check-config` validates local settings; use `arsenkin_status` separately to check account connectivity.
## Connect your assistant
Add this configuration to an MCP client that uses `mcpServers`, such as Claude Desktop or Cursor. Replace `/ABSOLUTE/PATH/` with your absolute path; Windows JSON paths can use forward slashes, such as `D:/Tools/`.
```json
{
"mcpServers": {
"arsenkin": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/arsenkin-mcp",
"run",
"--frozen",
"--extra",
"standalone",
"arsenkin-mcp",
"--config",
"/ABSOLUTE/PATH/arsenkin-mcp/mcp.local.json"
]
}
}
}
```
The client starts the MCP server for you. Refresh its tool list, then make your first request. For clients with a different config format, reuse the same `command` and `args`; `uv` must be available to the client process.
### First request
> Prepare an estimate for a frequency-check request with one phrase. Ask me for the region and frequency type; do not submit a paid job.
An estimate validates the request locally and shows the server cost estimate without calling Arsenkin. Submitting a paid job is a separate step. Cost estimates use configured policy units, not a guaranteed invoice amount or a ruble quote.
If tools do not appear, check the absolute path, whether the client can find `uv`, and the `--check-config` result. For access errors, check account credentials and permissions. [Installation and troubleshooting](INSTALL.md).
## Access and limits
For execution, configure paid-task limits and approval, then run the worker with the same config and SQLite state. Exported values keep their JSON paths; specialised SEO tables are not generated automatically. Cancelling a job does not guarantee a refund for work already submitted.
Authenticated HTTP is available for a server deployment. See [HTTP setup](INSTALL.md#http), [configuration and permissions](docs/RUNTIME.md) and [Python package integration](INSTALL.md#python-package-and-platform-integration).
<details>
<summary>For developers: project checks</summary>
```sh
uv sync --frozen --all-groups --extra standalone
uv run --frozen --extra standalone python scripts/verify.py
uv run --frozen --extra standalone python scripts/verify_install.py
```
Tests use synthetic fixtures. A passing test run does not establish live provider connectivity.
</details>
## Built by ZAI.ONE
[ZAI.ONE](https://zai.one) is a digital agency working on websites, SEO, advertising and analytics. We also build tools that connect AI assistants to everyday work. [Talk to us on Telegram](https://t.me/zai_one) about setup, automation or an integration for your team.
## Use and feedback
You may install and use this project for your own accounts under [LicenseRef-ZAI-ONE](LICENSE).
This is not an open-source license. Third-party notices remain in [NOTICE](NOTICE).
If it helps, give the repository a ⭐. Missing something or found a bug? [Open an issue](https://github.com/zai-one/arsenkin-mcp/issues/new/choose).
I'm working on this project; accepted improvements are implemented here. Support is not guaranteed.
TDQS
Scored across 15 tools
Most tools occupy distinct workflow stages (estimate, prepare, submit, status, result, cancel), and single/batch variants are clearly labeled. A few near pairs like arsenkin_status vs arsenkin_task_status and get_result vs export_result could confuse an agent, though the descriptions do separate them.
All names share the arsenkin_ prefix and snake_case, and paired batch tools follow a clear modifier pattern. However, conventions are mixed: nouns like status and profile_catalog sit alongside bare verbs like prepare, submit, and cancel, plus get_/export_ prefixes in get_result and export_result, so the set is readable but not uniformly verb_noun.
Fifteen tools is on the upper end of a well-scoped set, but every tool addresses a distinct stage of the paid-task lifecycle, including estimates, approvals, submission, retrieval, export, and cancellation. Batch variants add count but are justified by the server's explicit batch workflow.
The core lifecycle is well covered: estimate, prepare, submit, task_status, get_result, export_result, and cancel, for both single and batch paths. The main gaps are lack of a general job-list or discovery tool and no batch-specific status/cancel endpoint, but agents can likely work around these.