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.
A curated public baseline; every entry carries a verification level and risk labels. 25 from the automated pipeline
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.
Runtime pattern before adoption
AI may help filter, explain, and draft a trial plan, but must not perform write, shell, external side effects, or production publishing before review.
Visible states
- Collect task context and platform constraints
- Preview setup, source, and permission boundaries
- Trial in a sandbox or low-permission environment
- Wait for human review before continuing
User controls
- Revise task/platform filters
- Add to Compare
- Open source for review
- Cancel high-risk adoption
- Submit pending/staging evidence
Approval gates
- Token/API key scope approval
- Approval before writes or state mutation
- External network target approval
- Auth-gated path not fully tested
Failure recovery
- Auth path needs manual reproduction or a constrained test account.
Trial acceptance
- Can a user find a task-fit candidate within two minutes
- Can the UI explain why it is recommended and where it does not fit
- Can the user identify token, write, shell, network, or local file risk
- Can the user separate L1/L2 evidence from full runtime proof
- Does a failed trial have fallback or manual takeover
Verification evidence
The matrix shows passed, partial, skipped, and not-tested boundaries. It is not production adoption approval.
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.
Trust profile
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.