What is openviking-controlplane?

A command-line tool for managing OpenViking collections, which are libraries of stored data, through a control-plane service. It also handles enterprise-library users and billing settings.

Use it to list, create, inspect, update, check usage for, or delete collections; retrieve a data-plane API key; manage library users; and configure billing.

It gives operators one interface for administering libraries and their access or billing configuration.

openviking-controlplane — Skill by volcengine/mcp-server

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add volcengine/mcp-server --skill openviking-controlplane

Clone the repo

git clone --depth 1 https://github.com/volcengine/mcp-server

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/volcengine/mcp-server/openviking-controlplane/github.svg)](https://agentmods.dev/skills/volcengine/mcp-server/openviking-controlplane)

Your own site

<a href="https://agentmods.dev/skills/volcengine/mcp-server/openviking-controlplane"><img src="https://agentmods.dev/badge/skills/volcengine/mcp-server/openviking-controlplane/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/volcengine/mcp-server/openviking-controlplane"><img src="https://agentmods.dev/badge/skills/volcengine/mcp-server/openviking-controlplane.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 185 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,973 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Data Exfiltration · line 21
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00185	$0.02973
Opus 5.5	$0.00074	$0.01189
Sonnet 5.5	$0.00037	$0.00595
Haiku 4.5	$0.00018	$0.00297

Measured 1mo ago against content hash 477c30954274, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

openviking-controlplane scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

server/mcp_server_openviking_controlplane/skills/openviking-controlplane/SKILL.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OpenViking Control Plane (`ov-cp`)

ov-cp is the CLI for the OpenViking control plane (topapi). It manages OV collections (libraries) and shares its core with the mcp-server-openviking-controlplane MCP server, so behavior is identical.

Setup

Install once (from the package dir): uv sync (or pip install -e .).

Configure via env vars (CLI flags -k / -e / --project override them):

Env var	Meaning	Default
`AGENTPLAN_API_KEY`	Ark AgentPlan ApiKey (sent as `Authorization: Bearer`)	— (required)
`VIKING_ENDPOINT`	Control-plane base URL	`https://api.vikingdb.cn-beijing.volces.com/openviking`
`OPENVIKING_PROJECT`	Default project	`default`
`VIKING_EXTRA_HEADERS`	Extra request headers, comma-separated `Key: Value`	—

export AGENTPLAN_API_KEY=ark-xxxxxxxx
# VIKING_ENDPOINT defaults to the public gateway — leave it unset for normal use.

The default endpoint is the reserved public gateway (not open yet). Override VIKING_ENDPOINT (or -e) only for testing — e.g. point it at a kubectl port-forward of the data-plane service. The full request URL is {endpoint}/api/openviking/{Action}.

Commands

ov-cp list                       # list collections (optionally --project X)
ov-cp get     <ResourceID>       # collection info (Status, models, version, ...)
ov-cp usage   <ResourceID>       # file counts / hourly CNY and AgentPlan AFP estimate
ov-cp usage   <ResourceID> --account-id team-alpha
ov-cp usage   <ResourceID> --user-id alice  # user in account default
ov-cp usage   <ResourceID> --account-id team-alpha --user-id alice
ov-cp api-key <ResourceID>       # default user's plaintext data-plane key
ov-cp api-key <ResourceID> --user-id alice  # selected user's plaintext key
ov-cp api-key <ResourceID> --account-id team-alpha --user-id alice
ov-cp create  --name my_kb       # create a collection (see below)
ov-cp update  <ResourceID> --description "..."   # update fields / switch billing
ov-cp update  <ResourceID> --model-api-key ark-xxx  # overwrite AgentPlan model key
ov-cp delete  <ResourceID> --yes # delete (irreversible; uninstalls the Helm release)

# users of an enterprise-tier library (key must be associated with the library):
ov-cp user list     <ResourceID>                     # users (ApiKey is masked)
ov-cp user list     <ResourceID> --role user --page 1 --limit 20
ov-cp user register <ResourceID> alice            # new users always get role=user
ov-cp user update   <ResourceID> alice --regenerate-key
ov-cp user delete   <ResourceID> alice --yes      # revoke a user's credential

# target a non-default data space with --account-id:
ov-cp user list     <ResourceID> --account-id team-alpha
ov-cp user register <ResourceID> alice --account-id team-alpha
ov-cp user update   <ResourceID> alice --account-id team-alpha --regenerate-key
ov-cp user delete   <ResourceID> alice --account-id team-alpha --yes

# data spaces (accounts) of an enterprise-tier library:
ov-cp account list   <ResourceID> --keyword team --page 1 --limit 20
ov-cp account create <ResourceID> team-alpha
ov-cp account delete <ResourceID> team-alpha  # prompts before cascading deletion

Read the full file on GitHub · 215 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago Changed · +56 lines · +21 tokens per session 477c30954274
1mo ago First seen · 159 lines · 164 tokens per session scan A 0b259a392cad

Subscribe to this mod's changes

openviking-controlplane is a skill published in the GitHub repository volcengine/mcp-server (330 stars, last pushed 10d ago), licensed MIT. It adds 185 tokens to every session and 2,973 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI RAG and vectors

embeddings

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

ruvnet/ruflo · 62 tokens

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

9router-embeddings

Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

decolua/9router · 66 tokens

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

foryourhealth111-pixel/Vibe-Skills · 30 tokens

embedding-strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

foryourhealth111-pixel/Vibe-Skills · 37 tokens