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AsterMind MCP server

by AsterMindAI·io.github.AsterMindAI/astermind-mcp·v0.1.0
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On-device reranking that cuts RAG context tokens ~67% while keeping a relevant passage.

A91/100grade A
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Safety scan
A91/100

full report

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If you have run it, two minutes of your experience saves the next person an afternoon.

AsterMind MCP tools (10, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • classify_text

    Classify text into your categories using an on-device ELM trained on the labeled examples you provide. Requires at least one example per category. Returns a confidence and honestly flags low-confidence results. Use this to route/label locally instead of paying an LLM to classify.

  • compare_texts

    Cosine similarity (0..1) between two texts using local embeddings. Best for detecting near-duplicates / paraphrase overlap, not fine-grained semantic ranking.

  • compress_context

    One-shot token saver: filter candidate documents to what is relevant and return a compact, numbered context block ready to paste into a prompt, plus token-before/after accounting.

  • count_tokens

    Count tokens in a string or array of strings using an OpenAI-compatible BPE tokenizer (o200k/cl100k family). Useful for measuring context size and budgeting.

  • detect_language

    Coarse language detection over 6 European languages (English, Spanish, French, German, Italian, Portuguese) using an on-device classifier. Runs locally with no API call.

  • estimate_savings

    Given the full context vs the filtered context you plan to send an LLM, compute tokens removed, percent saved, and (optionally) dollars saved per call for a given model input price.

  • filter_contextwrite action

    Drop documents not relevant to the query so you feed the LLM a small, relevant context instead of everything. Reports exact tokens saved using an OpenAI-compatible tokenizer. This is the core token-reduction tool.

  • generate_embeddings

    Produce deterministic local char-level embeddings for a list of texts. Good for clustering, dedupe, and near-duplicate detection. Not a substitute for large transformer embeddings on nuanced semantics.

  • rerank_documents

    Rank candidate documents/chunks by relevance to a query using an on-device ELM reranker. Best when the query shares vocabulary with the documents (typical RAG). Lexical (TF-IDF) based: strong on keyword overlap, weaker on pure-synonym matches.

  • semantic_search

    Return the top documents matching a query from a provided set, scored by an on-device lexical (TF-IDF) reranker. Strong on keyword overlap; not a transformer embedding search.

Public scan report

scanner v0.1.9 · 2026-09-25 · same rubric, same numbers if you re-run it

no findings
  • Code scan6 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 0 days ago15/15
  • Maintainer identityregistry namespace matches repository owner7/10
Overall 91/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How AsterMind MCP compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
91/100
higher than 88% of 4,460 AI and agents servers
Tools
10
the typical AI and agents server has 6
Tools that change things
10%
typical: 0%. Fewer write tools means less an agent can break

What its tools let an agent do: 8 read · 1 create · 1 change

Install AsterMind MCP in Claude Code, Cursor or VS Code

Runs npx -y @astermind/astermind-mcp on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add astermind-mcp -- npx -y @astermind/astermind-mcp
Add to Cursor

What the publisher says

From the AsterMind MCP repository's README, as published. We do not edit it. Read it on GitHub

AsterMind MCP

Cut the tokens you send to an LLM — on your own machine, before the call goes out.

AsterMind MCP is a Model Context Protocol server that ranks and filters your retrieved context locally, so you send the model the few passages that actually answer the question instead of everything your retriever returned. It runs on the AsterMind Community Edition ELM engine. No network calls, no API keys, no data leaves the machine.

What it actually does (measured, not promised)

On a 6-scenario RAG benchmark (support KB, API docs, HR policy, e-commerce FAQ, DevOps runbook, fintech help), filtering 10 retrieved candidates down to the top 3 by rerank score:

Tokens counted with gpt-tokenizer (o200k/cl100k BPE, OpenAI-compatible). Reproduce it yourself: npm run benchmark. Full methodology and per-scenario results are in BENCHMARK.md.

The honest tradeoff

Savings and recall move in opposite directions — keep fewer chunks, save more tokens, risk dropping a relevant one. You choose the point on the curve:

Rank-1 surfaced a relevant passage in 100% of scenarios at every K we tested. That is the strong, defensible result. For single-hop questions (FAQ, support, one-fact lookups) keep top 1–3 and cut ~67–88% of context. For multi-hop questions that need several passages, keep top 5–6 — don't over-filter, or you'll drop a needed passage.

Install

npx @astermind/astermind-mcp

Or add it to your MCP client config (Claude Desktop, Cursor, VS Code, Cline):

{
  "mcpServers": {
    "astermind": {
      "command": "npx",
      "args": ["-y", "@astermind/astermind-mcp"]
    }
  }
}

Runs on Node 18+. No GPU, no build step, no configuration.

Tools (10)

Every tool returns a confidence signal. Where the engine is weak, the tool says so rather than guessing silently.

Known limitations (stated on purpose)

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

AsterMind MCP: common questions

Is AsterMind MCP server safe?
Yes, by our scan: it is graded A (91/100). Read the AsterMind MCP safety report
How do I install AsterMind MCP?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does AsterMind MCP need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is AsterMind MCP maintained?
The last commit was 40 days ago (2026-09-25). The latest release is v0.1.0.
What can I use instead of AsterMind MCP?
Servers from other publishers that do the same job: agent-device MCP server, agent-device MCP server and Commonly MCP server.
How does AsterMind MCP compare with other AI and agents MCP servers?
safety score 91/100 (higher than 88% of 4,460 AI and agents servers); tools 10 (the typical AI and agents server has 6); tools that change things 10% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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