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.
npx skills add withoneai/one-agent-plugin --skill convex-managementgit clone --depth 1 https://github.com/withoneai/one-agent-pluginWrote 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.
[](https://agentmods.dev/skills/withoneai/one-agent-plugin/convex-management)<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/convex-management"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/convex-management/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.
<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/convex-management"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/convex-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00120 | $0.02070 |
| Opus 5 | $0.00060 | $0.01035 |
| Sonnet 5 | $0.00024 | $0.00414 |
| Haiku 4.5 | $0.00012 | $0.00207 |
Grade A, and why
convex-management 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convex Management through One
Convex Management provides a developer console for managing applications built on the Convex backend platform, including database data, serverless functions, deployments, logs, and configuration, helping teams monitor, operate, and scale real-time applications from a centralized interface.
One exposes Convex Management through four MCP tools. The table below carries real action ids from One's knowledge base, so for a common operation you can skip search and go straight to reading the action's parameters.
How to run an action
- Find the action in the table below, or call
search_one_platform_actionswith platformconvex-managementif it is not listed. - Call
get_one_action_knowledgewith the action id. Do this every time, including for actions in this table. The table gives you the id, not the parameters. - Call
execute_one_actionwith parameters copied from that knowledge.
Never guess a parameter name, a body field, or an enum value. The knowledge has the real schema, and a guessed field is either a 400 or a silent write of the wrong thing.
Before you start
Call list_one_integrations once and confirm Convex Management is connected. If it is missing, the user has not connected it: say so and point them at https://app.withone.ai rather than reaching for raw HTTP.
Each connection carries an access field. If it reports {"policy": "methods", "methods": ["GET"]} the agent is read-only here, so plan a read-only answer instead of attempting a write that will be refused.
Before a write
Creates, updates, deletes and sends land on a real Convex Management account and cannot be recalled. State the action and the specific target in one line before the first write in a task, and let the user stop you. Reads need no confirmation.
Actions
Deployments
| Action | Method | Path | Action id |
|---|---|---|---|
| Get a Deployment | GET | /deployments/{{DEPLOYMENT_NAME}} |
conn_mod_def::GJj9jKe8ihs::K8WdOzgqTjWCtRGtHQg1IQ |
| List a Project’s Deployments | GET | /projects/{{PROJECT_ID}}/list_deployments |
conn_mod_def::GJj9iYmynbw::wl2zkUx4R1mFZLMQ9R8-Ew |
| Delete a Deployment | POST | /deployments/{{DEPLOYMENT_NAME}}/delete |
conn_mod_def::GJj9jEHxxSA::1rM03XibTwiC_EihkDAfIQ |
| Delete a Deployment's Custom Domain | POST | /deployments/{{DEPLOYMENT_NAME}}/delete_custom_domain |
conn_mod_def::GJj9kITJIG8::I1yeqWs5QiqYhNEqPZ4mqA |
| Update a Deployment's Settings | PATCH | /deployments/{{DEPLOYMENT_NAME}} |
conn_mod_def::GJj9jQbiL3c::Hke8RHCUQ0Oe-HJ8yXYyPQ |
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.
- 5d ago First seen · 125 lines · 0 tokens per session scan A f3576f032f00
convex-management is a skill published in the GitHub repository withoneai/one-agent-plugin (1 stars, last pushed 19d ago), licensed MIT. It adds 120 tokens to every session and 2,070 once invoked, about $0.0006 per session on Opus 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 2026-09-03.
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