Agentset is an open-source platform for building retrieval-augmented generation applications, which let AI systems find relevant information from document collections before generating responses. Developers use it for ingestion, vector search, evaluation, cited chat, hosting, and agentic applications through APIs and SDKs. Its catalogue rules and skills provide agent guidance for working with Agentset.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/agentset-ai/agentsetWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/mcp/agentset-ai/agentset/context7)<a href="https://agentmods.dev/mcp/agentset-ai/agentset/context7"><img src="https://agentmods.dev/badge/mcp/agentset-ai/agentset/context7/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/mcp/agentset-ai/agentset/context7"><img src="https://agentmods.dev/badge/mcp/agentset-ai/agentset/context7.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
context7 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 21d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to context7 — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
{
"context7": {
"command": "npx",
"args": [
"-y",
"@upstash/context7-mcp"
]
}
}What else mcp.json configures
This page is one entry in a file that holds 2. Installing the file brings all of them; each is measured and scanned on its own page.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 21d ago First seen · 9 lines scan A 6f9d71bbe560
context7 is an MCP server published in the GitHub repository agentset-ai/agentset (2,111 stars, last pushed 2d ago), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. A static security scan graded it A with 0 findings. It is 86% identical to context7, differing in 2 lines, and is treated as a copy.
Other mcp servers, from other repositories
re-call
Trustworthy retrieval for an AI agent's own memory — verdict, confidence and provenance per hit, or an explicit abstention. Runs locally from the recall-rag Python package. Needs 3 environment variables to run.
coderag
Locus — local-first hybrid code search MCP (transitional id @sylphx/coderag-mcp; brand @sylphx/locus). Runs locally from the @sylphx/coderag-mcp npm package. Needs 3 environment variables to run.
velesdb-memory
MCP server "velesdb-memory" as configured in cyberlife-coder/VelesDB. Launched with https://github.com/cyberlife-coder/VelesDB/releases/download/velesdb-memory-v0.9.
forge
MCP server for Forge, Voxell's embedding API. Voxell's Ingot-8B-R3 ranks #1 for English on MTEB. Runs locally from the @voxell/forge-mcp npm package. Needs 1 environment variable to run.
shelfmark-rag
Local hybrid retrieval (BM25 + embeddings + RRF) for agent memory and codebases — one SQLite file, MCP server included. Runs locally from the shelfmark-rag Python package.
mcp-docs-ask
MCP server for local retrieval over markdown documentation. Runs locally from the mcp-docs-ask Python package.