GitHub MCP Server
MCP Server · Coding, Automation
66/100
Rec
Connect agents to GitHub repositories, issues, pull requests, and code search.
Interface-verifiedhigh risk
Explore / mcp-use Agent Library
Agent TemplateRisk reviewedmedium risk
mcp-use Agent Library
mcp-use Agent Library is an agent template for automation and coding workflows on Claude, Claude Code, Codex and self-hosted. Verified from source metadata and risk-reviewed by AgentMaps.
Preview · 94
Preview: the curated catalog plus the latest auto-checked entries. 47 newly auto-checked
60/100
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 60/100, fits Automation, setup is easy.
Best for
Automation workflows where repeatable tool calls are more useful than one-off prompts.
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
Requires or commonly uses an API token. Use least-privilege scopes, keep tokens out of prompts/logs, and rotate credentials after testing.
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
mcp-use Agent Library is an agent template for automation and coding workflows on Claude, Claude Code, Codex and 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
- - Automation workflows where repeatable tool calls are more useful than one-off prompts.
- - Coding workflows that need reusable tool or agent integration.
- - Teams evaluating agent frameworks before committing to a custom implementation.
Best for
- - Automation workflows where repeatable tool calls are more useful than one-off prompts.
- - Coding workflows that need reusable tool or agent integration.
- - Teams evaluating agent frameworks before committing to a custom implementation.
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.
- - Environments where API keys or service tokens cannot be scoped or rotated.
Limitations
- - Production use without reviewing the upstream documentation and permission scope.
- - Users who require full install, execution, and interface verification before trying a tool.
- - Environments where API keys or service tokens cannot be scoped or rotated.
How it runsAgentic workflow
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
- Sandbox approval before shell execution
- 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.
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.
Interface-verified
Interface or entrypoint parsed; not a production safety approval.
- 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 60, based on automated evaluation.
- Trust metadata came from read-only GitHub live metadata enrichment.
Evaluation summary
Detailed checkNeeds reviewTrigger evalMisfire risk: High
Best for
- - Automation tasks with explicit source and risk review
- - Coding 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
- - Requires Token
- - MCP Server Dependent
- - External Network
- - Credential or sensitive token access is required or implied.
- - External network access is present 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.
- mediumnetwork
External network access is present or implied.
Recommendation: Keep network assumptions visible and source-backed.
Quality findings
- mediumstructure
Skill/agent asset is represented by metadata or curated notes, not a parsed SKILL.md.
Recommendation: Add a reviewable SKILL.md with frontmatter, trigger guidance, boundaries, and evidence links.
Known conflicts
- - May overlap with generic agent, workflow, or automation templates.
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 & integration58
Interface quality60
Follows standards76
Task fit60
Works across platforms56
Why it scores well
- - Automation workflows where repeatable tool calls are more useful than one-off prompts.
- - Coding workflows that need reusable tool or agent integration.
- - Teams evaluating agent frameworks before committing to a custom implementation.
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.
- - Environments where API keys or service tokens cannot be scoped or rotated.
Alternatives
Playwright MCP
MCP Server · Browser, Automation
84/100
Rec
Expose browser automation primitives through MCP for web navigation and testing.
Interface-verifiedmedium risk
Context7 MCP
MCP Server · Coding, Research
87/100
Rec
Fetch current library documentation and examples directly into coding agents.
Interface-verifiedlow risk