Connect agents to GitHub repositories, issues, pull requests, and code search.
Supabase MCP
Give agents controlled access to Supabase projects, schemas, SQL, and project metadata.
Preview: the curated catalog plus the latest auto-checked entries. 47 newly auto-checked
What it is good for
Give agents controlled access to Supabase projects, schemas, SQL, and project metadata. AgentMaps treats this as a mcp server candidate and scores it with a capped benchmark score plus a separate recommendation score that includes trust, platform fit, setup preference, and risk preference.
Use cases
- - Query structured data
- - Transform records
- - Generate summaries
- - Review code changes
- - Inspect repository context
Best for
- - Developers using Self-hosted for data workflows.
- - Teams that want visible setup, verification, and risk evidence before adoption.
Not for
- - Production write access without sandboxing or human approval.
- - Users who cannot provide the required credentials.
Limitations
- - Scenario-level L5-L7 benchmark testing is not part of the current MVP record.
How to use it & what to watch
AI may help filter, explain, and draft a trial plan, but must not perform write, shell, external side effects, or production publishing before review.
What you'll see
- Start from your task and the platform you use
- Review its setup, source, and permission scope
- Try it first in an isolated or low-permission environment
- For high-risk actions, wait for a review before continuing
Where you can step in
- Adjust task/platform filters
- Add to compare
- Open the source to check
- Stop a high-risk adoption
- Add what you've learned
Actions needing approval
- Token/API key scope approval
- Approval before writes or state mutation
- External network target approval
- Login-required path not fully tested
If something goes wrong
- Auth path needs manual reproduction or a constrained test account.
How to judge a trial
- Can you find a task-fit candidate within two minutes
- Can it explain why it's recommended and where it doesn't fit
- Can you see the token, write, shell, network, or local-file risks
- Can you tell 'checked the docs' apart from 'actually verified in a run'
- If a trial fails, is there a fallback or a way to take over manually
Verification evidence
Below is the result of each check — passed, partial, skipped, or not yet tested. It shows how far checking has gone, not that the capability is cleared for production use.
Source, docs, license, or package metadata exists.
Static review assigns permission and risk boundaries.
Install path can be checked, but not necessarily in your environment.
Interface or entrypoint parsed; not a production safety approval.
- Seed profile normalized into AgentMaps schema.
- Source and documentation fields are present.
- Static risk flags are assigned.
- Install path is represented for harness checks.
- Interface parsing pending.
- Benchmark score capped at 80 by L3 verification.
Evaluation summary
Risk findings
- - Requires Token
- - Database Access
- - Write Permission
Verified evidence
- - Seed profile normalized into AgentMaps schema.
- - Source and documentation fields are present.
- - Static risk flags are assigned.
- - Install path is represented for harness checks.
Score breakdown
Why it scores well
- - Clear task fit for the selected scenario.
- - Static verification evidence is available.
- - Portable across multiple AI clients.
Watch outs
- - Requires credentials before full testing.
- - High-risk permissions need sandboxing.
Alternatives
Expose browser automation primitives through MCP for web navigation and testing.
Fetch current library documentation and examples directly into coding agents.