AsterMind MCP
NPM · @ASTERMIND/ASTERMIND-MCP · SCANNED SEP 26
On-device reranking that cuts RAG context tokens ~67% while keeping a relevant passage.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 97 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 0 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability58
- AI-judged instruction clarity (fair).Partial
- Tool/resource definitions use about 728 tokens (~72/item across 10 items; 10 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage77
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 32% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 10 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 10 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
How do I install the AsterMind MCP server?
AsterMind MCP runs locally as an npm package, launched with npx -y @astermind/astermind-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · @astermind/astermind-mcp
claude mcp add astermindai-astermind-mcp -- npx -y @astermind/astermind-mcp
{
"mcpServers": {
"astermindai-astermind-mcp": {
"command": "npx",
"args": [
"-y",
"@astermind/astermind-mcp"
]
}
}
} {
"servers": {
"astermindai-astermind-mcp": {
"command": "npx",
"args": [
"-y",
"@astermind/astermind-mcp"
]
}
}
} codex mcp add astermindai-astermind-mcp -- npx -y @astermind/astermind-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"astermindai-astermind-mcp": {
"type": "local",
"command": [
"npx",
"-y",
"@astermind/astermind-mcp"
],
"enabled": true
}
}
} openclaw mcp add astermindai-astermind-mcp --command npx --arg -y --arg @astermind/astermind-mcp
mcp_servers:
astermindai-astermind-mcp:
command: "npx"
args: ["-y", "@astermind/astermind-mcp"] {
"McpServers": {
"astermindai-astermind-mcp": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"@astermind/astermind-mcp"
]
}
}
} assistant mcp add astermindai-astermind-mcp -t stdio -c npx -a -y @astermind/astermind-mcp
{
"mcpServers": {
"astermindai-astermind-mcp": {
"command": "npx",
"args": [
"-y",
"@astermind/astermind-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 25 Sept 26 65
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 26 Sept 2026 · Analysed npm/@astermind/astermind-mcp@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 97 packages
| Packages resolved | 97 |
|---|---|
| Stale | 31 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
classify_text Classify text (train-from-examples) ~99
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.
| Name | Type | Req | Description |
|---|---|---|---|
| categories | array | yes | The category labels. |
| examples | array | yes | Labeled training examples (>= 1 per category). |
| text | string | yes | Text to classify. |
No output schema declared.
No examples provided.
compare_texts Compare two texts (near-duplicate similarity) ~58
Cosine similarity (0..1) between two texts using local embeddings. Best for detecting near-duplicates / paraphrase overlap, not fine-grained semantic ranking.
| Name | Type | Req | Description |
|---|---|---|---|
| text1 | string | yes | – |
| text2 | string | yes | – |
No output schema declared.
No examples provided.
compress_context Compress context into a ready-to-paste block ~73
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.
| Name | Type | Req | Description |
|---|---|---|---|
| documents | array | yes | – |
| maxDocs | integer | – | – |
| minRelevance | number | – | – |
| query | string | yes | – |
No output schema declared.
No examples provided.
count_tokens Count tokens ~47
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.
| Name | Type | Req | Description |
|---|---|---|---|
| text | – | yes | – |
No output schema declared.
No examples provided.
detect_language Detect language ~50
Coarse language detection over 6 European languages (English, Spanish, French, German, Italian, Portuguese) using an on-device classifier. Runs locally with no API call.
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | – |
No output schema declared.
No examples provided.
estimate_savings Estimate token & cost savings ~71
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.
| Name | Type | Req | Description |
|---|---|---|---|
| filteredContext | – | yes | – |
| fullContext | – | yes | – |
| usdPerMillionInputTokens | number | – | – |
No output schema declared.
No examples provided.
filter_context Filter context to relevant docs (token saver) ~105
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.
| Name | Type | Req | Description |
|---|---|---|---|
| documents | array | yes | – |
| maxDocs | integer | – | Max docs to keep (default 5). |
| minRelevance | number | – | Min relevance 0..1 to keep a doc (default 0.5). |
| query | string | yes | – |
No output schema declared.
No examples provided.
generate_embeddings Generate on-device embeddings ~51
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.
| Name | Type | Req | Description |
|---|---|---|---|
| texts | array | yes | – |
No output schema declared.
No examples provided.
rerank_documents Rerank documents by relevance ~107
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.
| Name | Type | Req | Description |
|---|---|---|---|
| documents | array | yes | Candidate documents/chunks to rank. |
| query | string | yes | The user query or question. |
| topK | integer | – | Return only the top K (default: all). |
No output schema declared.
No examples provided.
semantic_search Search a document set ~67
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.
| Name | Type | Req | Description |
|---|---|---|---|
| documents | array | yes | – |
| query | string | yes | – |
| topK | integer | – | – |
No output schema declared.
No examples provided.
What is the AsterMind MCP server?
AsterMind MCP is listed in the public MCP registry as io.github.AsterMindAI/astermind-mcp. On-device reranking that cuts RAG context tokens ~67% while keeping a relevant passage. This page covers its npm package (@astermind/astermind-mcp).
Is the AsterMind MCP server safe to use?
AsterMind MCP scores 65 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 26 September 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the AsterMind MCP server expose?
AsterMind MCP exposes 10 tools: rerank_documents, filter_context, compress_context, classify_text, detect_language, and 5 more. Their descriptions and schemas cost roughly 728 tokens of context every time the server is loaded.
Is the AsterMind MCP server still maintained?
AsterMind MCP is still listed as active in the MCP registry. We last reached this channel on 26 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the AsterMind MCP server under?
AsterMind MCP declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.