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MCP ServerRisk reviewedmedium risk

codebase-memory-mcp

codebase-memory-mcp is an MCP server for coding, research and data workflows on self-hosted. Verified from source metadata and risk-reviewed by AgentMaps.

Repository

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Preview: the curated catalog plus the latest auto-checked entries. 47 newly auto-checked

58/100

Recommended

Confidence: medium

65/100

Benchmark

Confidence: medium

Needs human reviewRisk reviewed · Verification tier is risk-reviewed; this reflects automated evidence, not a full install or execution test.

Why

Recommendation 58/100, fits Coding, setup is medium.

Best for

Coding workflows that need reusable tool or agent integration.

Not for

Production use without reviewing the upstream documentation and permission scope.

Boundary

Checked up to "Risk reviewed"; deeper install and interface testing isn't covered yet — confirm in your own environment.

Remaining risk

Risk is medium because this tool may rely on network access, credentials, browser automation, write actions, shell execution, or external services.

Next step

Review tokens, write access, and network scope in a sandbox before team adoption.

Check before you adopt

This capability uses higher-risk permissions (tokens, write access, shell, and the like). Try it in an isolated environment and confirm what it actually does before using it for real work.

What it is good for

codebase-memory-mcp is an MCP server for coding, research and data workflows on self-hosted. Verified from source metadata and risk-reviewed by AgentMaps. This profile is auto-verified by the AgentMaps daily pipeline and passed publish review before being listed.

Use cases

- Coding workflows that need reusable tool or agent integration.
- Research workflows that need retrieval, summarization, or structured source gathering.
- Data workflows where source access and credential scope can be reviewed before use.
- Users who already run an MCP-compatible client such as Claude Code, Codex, Cursor, Cline, or a self-hosted agent.

Best for

- Coding workflows that need reusable tool or agent integration.
- Research workflows that need retrieval, summarization, or structured source gathering.
- Data workflows where source access and credential scope can be reviewed before use.
- Users who already run an MCP-compatible client such as Claude Code, Codex, Cursor, Cline, or a self-hosted agent.

Not for

- Production use without reviewing the upstream documentation and permission scope.
- Users who require full install, execution, and interface verification before trying a tool.

Limitations

- Production use without reviewing the upstream documentation and permission scope.
- Users who require full install, execution, and interface verification before trying a tool.

How it runsTool/runtime connector

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

Approval before writes or state mutation
Sandbox approval before shell execution
External network target approval

If something goes wrong

On trial failure, fall back to source docs, alternatives, or submit what you found for review.

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.

Info checked

Source, docs, license, or package metadata exists.

passed

Risk reviewed

Static review assigns permission and risk boundaries.

passed

Install-verified

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

skipped

Interface-verified

Interface or entrypoint parsed; not a production safety approval.

skipped

Verification tier is risk-reviewed; this reflects automated evidence, not a full install or execution test.
Static audit status is passed; the source code was reviewed for risk signals but not cloned, installed, or executed.
Benchmark score is 65 and recommendation score is 58, based on automated evaluation.
Trust metadata came from read-only GitHub live metadata enrichment.

Evaluation summary

Detailed check

Needs reviewTrigger evalMisfire risk: High

Best for

- Coding tasks with explicit source and risk review
- Research tasks with explicit source and risk review
- Data tasks with explicit source and risk review

Avoid when

- You need unattended publish, production write access, or bypassed manual review.
- Secrets or long-lived tokens cannot be scoped safely.

Risk findings

- Automated Discovery
- Unverified Source
- Credential or sensitive token access is required or implied.

Verified evidence

- L2 static benchmark status: passed

Safety findings

hightoken
Credential or sensitive token access is required or implied.
Recommendation: Require manual review and avoid browser-exposed secrets or NEXT_PUBLIC service keys.

Evidence references

Canonical source URL
Used only as source evidence; no source code was executed.
Normalized source artifact
Provides object type, platform fit, risk guess, risk flags, and publish blockers.
Static trigger prompt generation
Positive, negative, and ambiguous prompts generated from metadata; not a runtime model benchmark.
L2 benchmark run
Benchmark status: passed.

Score breakdown

Safety & permissions64

How well it works70

Actively maintained74

Setup & integration72

Interface quality60

Follows standards76

Task fit58

Works across platforms14

Why it scores well

- Coding workflows that need reusable tool or agent integration.
- Research workflows that need retrieval, summarization, or structured source gathering.
- Data workflows where source access and credential scope can be reviewed before use.
- Users who already run an MCP-compatible client such as Claude Code, Codex, Cursor, Cline, or a self-hosted agent.

Watch outs

- Production use without reviewing the upstream documentation and permission scope.
- Users who require full install, execution, and interface verification before trying a tool.