EN中
Explore / Postgres MCP
MCP ServerL3high risk

Postgres MCP

Let agents inspect and query PostgreSQL databases through a structured MCP interface.

RepositoryDocs
Curated baseline · 52

A curated public baseline; every entry carries a verification level and risk labels. 25 from the automated pipeline

63
Recommended
Confidence: medium
73
Benchmark
Confidence: medium
Needs human reviewL3 · Seed profile normalized into AgentMaps schema.
Why
Recommendation 63, fits Data, setup is medium.
Best for
Developers using Self-hosted for data workflows.
Not for
Production write access without sandboxing or human approval.
Boundary
Verified to L3; L4 not covered, so this is not full runtime proof.
Remaining risk
High-risk permissions require explicit user approval and sandboxing.
Next step
Review tokens, write access, and network scope in a sandbox before team adoption.
Review before adoption
This capability includes high-risk, token, write, shell, or auth-gated gaps. L4 means interface parsing, not production safety approval.

What it is good for

Let agents inspect and query PostgreSQL databases through a structured MCP interface. 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
  • - Run repeatable task flows
  • - Coordinate tool calls

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.
AI-native runtime contractTool/runtime connector

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

  • 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.

L1 Metadata

Source, docs, license, or package metadata exists.

passed
L2 Static audit

Static review assigns permission and risk boundaries.

passed
L3 Install path

Install path can be checked, but not necessarily in your environment.

passed
L4 Interface

Interface or entrypoint parsed; not a production safety approval.

skipped
  • 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

Heuristic estimate
Needs reviewstaticTrigger risk medium

Risk findings

  • - Database Access
  • - Sensitive Data
  • - 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

Safety47
Execution proxy78
Maintenance80
Setup75
Interface83
Standards76
Task fit82
Portability83

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

GitHub MCP Server
MCP Server · Coding, Automation
66
Rec

Connect agents to GitHub repositories, issues, pull requests, and code search.

L4high riskSelf-hosted
View details →
Playwright MCP
MCP Server · Browser, Automation
84
Rec

Expose browser automation primitives through MCP for web navigation and testing.

L4medium riskSelf-hosted
View details →
Supabase MCP
MCP Server · Data, Coding +1
62
Rec

Give agents controlled access to Supabase projects, schemas, SQL, and project metadata.

L3high riskSelf-hosted
View details →