planetscale-mcp-agent-operating-model — Claude Code skill
Configures safe agent operations with PlanetScale MCP. It lets agents inspect telemetry and open migration PRs while blocking direct changes to production.
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a claude-codeWhat planetscale-mcp-agent-operating-model does
The skill sets strict boundaries for coding agents using PlanetScale MCP. Agents prefer the insights-only server by default to inspect telemetry and schema recommendations. When a task requires the full MCP server, write tools remain disabled unless an operator grants explicit approval.
It guides teams to define database parameters inside a project AGENTS.md file, covering organization name, branch targets, and engine types like Vitess or Postgres. Autonomous agents can safely correlate query patterns with code, create development branches, apply DDL in isolation, and open deploy requests for human review.
Direct DDL execution, production data writes, credential rotation, and webhook creation are blocked by default. Because human review is required before merging or deploying, the database stays protected from uncontrolled mutations.
What it helps with
- block autonomous write queries against production databases
- prepare non-production branches and deploy requests from schema suggestions
- trace Insights regressions to source routes and repository commits
- audit query tag coverage across repository code
Example prompts for the agent
- Check recent schema recommendations via PlanetScale MCP and create a dev branch with the migration
- Correlate the latest Insights query regressions with our repository commits
How to install planetscale-mcp-agent-operating-model
1Skills CLI (Vercel)
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a claude-code2Manually: put the files in place
~/.claude/skills/planetscale-mcp-agent-operating-model/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/planetscale-mcp-agent-operating-model/
1Skills CLI (Vercel)
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a codex2Manually: put the files in place
.agents/skills/planetscale-mcp-agent-operating-model/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a cursor2Manually: put the files in place
.cursor/skills/planetscale-mcp-agent-operating-model/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install planetscale/skills planetscale-mcp-agent-operating-model2Manually: put the files in place
.github/skills/planetscale-mcp-agent-operating-model/SKILL.md1Skills CLI (Vercel)
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a gemini-cli2Platform CLI
gemini skills install https://github.com/planetscale/skills --path planetscale-mcp-agent-operating-modelManually: put the files in place
.devin/skills/planetscale-mcp-agent-operating-model/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about planetscale-mcp-agent-operating-model
How to install planetscale-mcp-agent-operating-model in Claude Code?
Run npx skills add planetscale/skills --skill planetscale-mcp-agent-operating-model -a claude-code. You can also place the file directly at ~/.claude/skills/planetscale-mcp-agent-operating-model/SKILL.md.
What does planetscale-mcp-agent-operating-model do?
It establishes safety guardrails for AI agents connecting to PlanetScale MCP. Agents can analyze telemetry and propose schema changes in development branches without directly modifying production.
Which actions are blocked for agents by default?
Agents cannot run write SQL queries on production, execute production DDL, deploy requests, merge PRs, or change network access rules without manual approval.
Source text
SKILL.md · MIT
---
name: planetscale-mcp-agent-operating-model
description: Configure safe agent behavior around PlanetScale MCP, Insights, schema recommendations, and repository work without autonomous production mutation.
---
# MCP agent operating model
## Purpose
Define how agents should use PlanetScale MCP safely. Agents should use production telemetry to generate useful work while avoiding autonomous production changes.
## Default MCP choice
Use the PlanetScale MCP insights-only server when the task only needs Insights and Schema Recommendations.
Use the full PlanetScale MCP server only when the task explicitly requires database/schema access beyond Insights. Prefer read-only scopes.
The full MCP server has query execution tools. Treat write query tools as disabled unless the operator explicitly approves a specific non-production action or a carefully reviewed production action.
## AGENTS.md guidance
Two different documents both named `AGENTS.md` serve different purposes:
1. **CLI agent guide** — shipped with `pscale` (`AGENTS.md` in the
[planetscale/cli](https://github.com/planetscale/cli) repo, or
`pscale agent-guide --format json`). Covers auth, `--format json`, flag
placement, and `pscale sql`. Load skill `planetscale-pscale-cli-automation` for the
same conventions inside this skills pack.
2. **Project agent guide** — your application repository's `AGENTS.md` (or
equivalent). Covers database targeting and approval policy for *this* app.
When working inside a repository, recommend adding a **project** database
targeting section to `AGENTS.md` or equivalent project instructions:
- PlanetScale organization.
- Database.
- Branch.
- Engine: Vitess or Postgres.
- Production branch name.
- Whether agents may use MCP insights-only or full MCP.
- Whether write queries are forbidden.
- Required approval protocol for schema, Traffic Control, webhooks, roles, and network changes.
Do not edit `AGENTS.md` without approval.
## Safe autonomous tasks
Allowed by default:
- Read Insights.
- Read schema recommendations.
- Read schema metadata.
- Read existing webhooks and Traffic Control configuration.
- Read branch metadata.
- Inspect repository code.
- Correlate query patterns with code.
- File issues.
- Open pull requests.
- Create development branches.
- Apply DDL and migrations to non-production development branches.
- Open deploy requests into branches protected by a review workflow.
- Draft Traffic Control budget proposals.
- Draft webhook receiver requirements.
Where a PR + deploy-request workflow exists, the default deliverable for a
schema recommendation is the complete reviewable unit: development branch
with the DDL applied, PR with evidence (fingerprint, metrics, expected
effect), and an open deploy request. The human action is the merge/deploy
decision, not shepherding the proposal into existence.
Not allowed by default (the review-gate actions and non-reviewable mutations):
- Execute write SQL against production.
- Execute DDL directly against production branches.
- Deploy a deploy request / apply schema to production.
- Merge pull requests.
- Create webhooks.
- Create or enforce Traffic Control budgets.
- Rotate credentials.
- Change roles.
- Change IP restrictions or private connectivity.
- Restore or promote branches.
## Agent loops
### Daily recommendation loop
1. Read open schema recommendations.
2. Read top Insights regressions.
3. Correlate with repository code.
4. Generate ranked issues or PRs.
5. Human reviews.
6. Human approves any database-affecting action.
### Anomaly loop
1. Receive or inspect anomaly.
2. Gather affected query patterns and tags.
3. Identify source route/job/deploy.
4. Produce incident note and proposed remediation.
5. If code fix is obvious, open PR.
6. If database change is needed, create a proposed change set only.
### Traffic Control loop
1. Identify unsafe traffic slice from Insights/tags.
2. Draft `warn` budget proposal.
3. Human approves creation.
4. Observe warnings.
5. Human approves enforce mode only after validation.
## Scheduled loops (cron / Automations)
The loops above run interactively. They can also run on a schedule with no
human in the loop, in two tiers. Tier 2 requires a standing authorization
per `../planetscale-autonomous-execution-mode/SKILL.md`; Tier 1 requires none.
Every scheduled loop, both tiers: re-read authorization at run start,
stream status to a configured delivery channel, persist a run log, and
avoid filing duplicates (do not re-file an issue that is already open
for the same fingerprint/recommendation ID).
### Tier 1 — propose through the review workflow (no authorization needed)
- **Recommendation-to-PR loop** (daily): list open schema recommendations
via MCP; for each new one matching the workflow (additive or destructive
— the PR review is the gate), create a development branch, apply the
DDL, open a PR with fingerprint, metrics, and expected effect, and open
the deploy request. The reviewable unit is complete when a human can
ship it with one merge/deploy action. Output: branch + PR + deploy
request per recommendation.
- **Regression watch** (hourly or per-deploy): compare top patterns
against a stored baseline (p50/p99, rows read, execution count); on
material regression, identify the deploy SHA from query tags and file
a report linking pattern to commit range. Output: report.
- **Tag coverage audit** (weekly): measure percentage of query time
carrying tags; list untagged high-cost patterns with likely code
paths; open or update a single tracking issue. Output: issue.
- **Anomaly triage** (webhook-triggered, not polled): on `branch.anomaly`,
gather affected patterns, classify probable cause, post triage note to
the incident channel. Output: triage note.
- **Posture drift check** (daily): diff current safe-migrations flags,
webhook config, role list, and backup schedule against the last
assessment report; report any drift. Output: report.
### Tier 2 — execute the review-gate action (standing authorization required)
- **Recommendation deployer** (daily, after the PR loop): deploy open
deploy requests that match the allowlist — typically "additive DDL,
PR approved or authored from an open recommendation, deploy with revert
window, max N per run" — then verify via schema read-back and an
Insights follow-up on the target fingerprint. Destructive DDL deploys
autonomously only when the authorization states a runtime-verifiable
bound (e.g. "drop only indexes with zero reads in 30 days, confirmed
via Insights at run time"). Where the org requires PR approval before
deploy, an approved PR satisfies the review gate and the authorization
covers only the mechanical deploy.
- **Branch hygiene** (weekly): delete development branches older than the
authorized age bound with no open deploy request; never touch
production or protected branches.
- **Warn-budget gardener** (weekly): create warn-mode Traffic Control
budgets for newly identified expensive slices matching the allowlist;
report warn counts on existing budgets. Enforce mode is never entered
autonomously unless the authorization names the specific budget.
- **Credential expiry enforcement** (daily): delete or flag passwords
past the authorized max age, only where the authorization lists the
affected roles and a rotation runbook exists.
### Loop anti-patterns
- Polling MCP on a cron for events webhooks already deliver — use the
webhook as the trigger; use cron for baselines, sweeps, and audits.
- A Tier 2 loop whose allowlist is an intent ("keep things healthy")
rather than bounded operations.
- Loops that mutate without a delivery channel for status.
- Unbounded fan-out: one run applying every open recommendation at once
with no per-run cap.
## Query execution safeguards
For read queries:
- Prefer replicas when available.
- `planetscale_execute_read_query` routes reads to replicas by default when a
branch has replicas configured (`use_replica: true`). Set
`use_replica: false` only when the task needs primary-read semantics, such
as checking immediately-after-write state or primary-only behavior.
- Add source tags/comments for agent work.
- Avoid unbounded scans.
- Avoid `EXPLAIN ANALYZE` on production unless explicitly approved.
- Limit result sizes.
- Avoid querying sensitive columns unless required and approved.
- For Postgres tables with row-level security, remember that the MCP read role
uses `pg_read_all_data` and does not bypass RLS. If a read query returns zero
rows or a zero count and the MCP response warns that RLS may be filtering
results, treat the result as policy-filtered/unknown until confirmed through
an approved path; do not conclude the table is empty.
- When debugging high CPU on Postgres, use Insights data sorted by CPU
usage (via MCP where available, or `sort=cpuTime` on the Insights API).
CPU time metrics are Postgres-only; do not ask for the same CPU-sorted
view on Vitess.
For write queries:
- Default is forbidden.
- If approved, prefer non-production branch.
- Require exact SQL review.
- Require rollback plan.
- Require branch and database name confirmation.
## Output
Return:
- Recommended MCP server choice.
- Required scopes.
- AGENTS.md instructions to add.
- Allowed autonomous work.
- Disallowed work.
- Proposed agent loops.
- Approval gates.
End with:
“No MCP write tools or database mutations have been used.”
© planetscale · MIT · planetscale/skills