Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated today
Install
Agent Radar
Overview
Description from the maker (original English)
Search, inspect, recommend, and explain rated AI tools through Agent Radar.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs in the cloud
- Transport: Streamable HTTP
- Category: Search & AI memory
- You need to fill in: X-Agent-Radar-LLM-API-Key
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on the maker’s server: what you ask it goes through their server.
- You fill in yourself:
X-Agent-Radar-LLM-API-Key
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- Runs in the cloudNothing to install; it connects over the internet
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Run this in your terminal
claude mcp add --transport http agent-radar https://agent-radar.zation1.workers.dev/api/mcp --header 'X-Agent-Radar-LLM-API-Key: your-value'Fill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"agent-radar": {
"url": "https://agent-radar.zation1.workers.dev/api/mcp",
"headers": {
"X-Agent-Radar-LLM-API-Key": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"agent-radar": {
"type": "http",
"url": "https://agent-radar.zation1.workers.dev/api/mcp",
"headers": {
"X-Agent-Radar-LLM-API-Key": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
X-Agent-Radar-LLM-API-Keysecret HTTP headerLLM provider API key used only by recommend_tools.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
We have not read this server’s tool list yet; we check endpoints in rotation.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account zation, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- … Not checked yet
Endpoint responds
Not checked yet; we check endpoints in rotation
- … Not checked yet
Sign-in with OAuth
Not checked yet; we check endpoints in rotation
- … Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint
Compared with others in Search & AI memoryi
- More stars than 0% of the other 7250 servers in this category
- Published to the registry more recently than 23% of the other 7250 servers in this category
- Basic checks equal to or better than 100% of the other 7250 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Agent Radar safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: X-Agent-Radar-LLM-API-Key
Does it run on my computer or in the cloud?
It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.9.2) reached the registry 4 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer★ 48,148MITUpdated 20 days ago
Install
Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated last month
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Agent Radar
Choose AI tools with evidence, not hype.
Agent Radar is a structured rating and recommendation knowledge base for AI Agents, Skills, MCP Servers, CLIs, Frameworks, and Prompts/Rules. It helps developers and coding agents answer a practical question: which tool fits this task, and what should I know before trusting it?
Instead of publishing another directory of links, Agent Radar turns fragmented ecosystem information into Tool Cards, explainable ratings, task-oriented recommendations, risk signals, and agent-readable data.
Why Agent Radar
The AI tooling ecosystem is growing faster than any one developer can evaluate it. Names and categories overlap, documentation quality varies, and popularity often says little about task fit, integration cost, maintenance, permissions, or trust.
Agent Radar adds a decision layer:
- Task fit over popularity. Recommendations start from the work to be done, not a global leaderboard.
- Evidence over marketing. Sources, update times, provenance, and confidence remain visible.
- Explanation over opaque ranking. Ratings and recommendations expose reasons, trade-offs, and unsuitable conditions.
- Safety before execution. Permission, trust, data-flow, and production-impact risks can tighten or block a recommendation.
- Built for humans and agents. The same reviewed knowledge is available through a Web UI, static data, HTTP endpoints, and MCP.
What You Get
Structured Tool Knowledge
Tool Cards describe capabilities, appropriate tasks, limitations, integration methods, maintenance, permissions, known risks, sources, and confidence in a consistent format across tool types.
Explainable Ratings
Ratings combine task fit, maintenance, documentation, integration cost, security risk, and evidence quality. Scores never replace the underlying evidence or risk context.
Task-Oriented Recommendations
Describe a development task, environment, preferred tool type, allowed permissions, and risk tolerance. Agent Radar returns candidates with fit reasons, risks, alternatives, and a conservative next action.
| Action | Meaning |
|---|---|
use | A suitable option can be included in the plan within its stated boundaries |
compare | Multiple options deserve a trade-off review |
ask_human | Permission, account, production, or uncertainty boundaries require confirmation |
avoid | The option or operation crosses an unacceptable safety or quality boundary |
no_reliable_match | The catalog cannot support a trustworthy recommendation |
Agent-Readable Decision Context
JSON, JSONL, HTTP, and MCP surfaces let coding agents search the catalog, inspect Tool Cards, request recommendations, and explain ratings without scraping a human-only page.
How It Works
reviewed public sources-> normalized Tool Cards and provenance -> explainable ratings and risk signals -> task-oriented retrieval and recommendation -> deterministic safety enforcement -> provider-backed evaluation and release gates -> Web, JSON/JSONL, HTTP API, and MCP
Collection is controlled by a Source Registry. Candidates pass normalization, validation, review, admission, and promotion gates before entering reliable recommendation data. Dynamic recommendations use an LLM for task interpretation and candidate selection, while local deterministic logic validates known tools and enforces safety boundaries.
The v0.7 data path also discovers the current top two public repositories for GitHub topic agent-skills and expands eligible skills/**/SKILL.md manifests into one Tool Card per Skill. Repository rank is discovery evidence, not quality evidence. Each current build keeps only successfully fetched Skill manifests; raw Skill text stays inside ingestion evidence and is never sent to the recommendation provider.
Who It Is For
- AI-first developers choosing among agents, Skills, MCP Servers, CLIs, and Frameworks for a concrete task.
- Coding-agent users who want tool selection to include reasons, permissions, risks, and traceable evidence.
- AI platform teams building a trusted, reusable catalog and decision layer for developers.
- Tool maintainers who want capabilities, limitations, and sources represented accurately.
Trust and Safety
Agent Radar is designed to support safer selection, not to authorize execution.
- Sources and field-level evidence remain traceable.
- Unknown, stale, or conflicting evidence lowers confidence.
- Permissions and data flows stay visible beside recommendation value.
- High-risk operations can require human confirmation even when a tool is a strong task match.
- Unknown-source code execution is treated conservatively and cannot be promoted by provider output.
- Critical safety cases block a reviewed release when they fail, are missing, or are not executed.
Agent Radar does not automatically install, authorize, or run third-party tools. It is not a substitute for a security scanner, dependency audit, organizational policy, or human review of production-impacting actions.
Ways to Use Agent Radar
Web UI
Browse and filter the reviewed catalog, describe a task, inspect recommendations, open detailed Tool Cards, and review evaluation evidence through the Tools and Evaluation workspaces.
Static Data
Reviewed bundles expose versioned Tool Cards, Rating Results, search indexes, Golden Queries, evaluation summaries, manifests, provenance, and quality evidence as JSON or JSONL artifacts.
HTTP API
The Worker exposes read-oriented endpoints for:
/api/search_tools/api/get_tool_card/api/recommend_tools/api/explain_rating/api/version
Installable Agent Skill
Install the repository-owned agent-radar Skill with the open Agent Skills CLI:
npx skills add zation/agent-radar --skill agent-radarFor Codex, you can also ask $skill-installer to install the Skill directly from:
https://github.com/zation/agent-radar/tree/main/skills/agent-radarThe Skill is local-first. Run its explicit sync command to download the latest compatible reviewed Tool Cards, Ratings, and Search Index into a versioned cache. The client verifies the v1 channel, release manifest, file sizes, SHA-256 checksums, and record schemas before atomically switching the active local release. Search, Tool Card inspection, rating explanation, and recommendation-context construction then run without MCP, without a provider key, and without transmitting the task. A failed update keeps the last verified release active. When invoked, the Skill announces that Agent Radar is in use; every successful client result carries source: "agent-radar-skill", and every Agent Radar-backed answer ends with the verified release ID and data version. Commit SHA remains available in the structured result for audit.
MCP
/api/mcp provides a stateless Streamable HTTP MCP interface built with the official TypeScript SDK v2 beta. It exposes search_tools, get_tool_card, recommend_tools, and explain_rating; all four tools are read-only with respect to tool execution and installation. /api/mcp_manifest exposes the same shared contracts for simpler HTTP integrations.
recommend_tools accepts its ordinary query and optional model as tool input. A per-request provider credential is sent only in the optional secret header X-Agent-Radar-LLM-API-Key; it is never part of the tool schema or response. The Worker may use its configured server-side fallback when that header is absent.
The remote-only Agent Radar MCP server is published in the official Registry as io.github.zation/agent-radar, backed by the production Streamable HTTP endpoint and evidence-bound GitHub OIDC publication. You can connect to and use it through either of these listings:
Production completion requires release identity convergence before functional smoke: bounded polling must observe the exact immutable tag and commit SHA from /api/version, then MCP initialize.serverInfo.version must equal the SemVer derived from that tag. The same gate runs again before Registry publication, so a previous edge version cannot pass as the new release.
Documentation
- Product Brief
- User Workflows
- System Architecture
- Data Model
- Rating Rules
- Recommendation Engine
- Evaluation Plan
- Security and Trust
- Deployment and Operations
- Web UI
- Delivery work
Development and Contributing
Use the Development Guide for local setup, development commands, data generation, evaluation, testing, and troubleshooting.
Coding agents and contributors must also read AGENTS.md before changing the project. It defines document authority, required validation, safe automatic actions, and changes that require human confirmation.