# Academic Research Kernel (pypi · academic-research-kernel)

Deterministic research state, evidence validation and statistical recomputation.

- Trust score: 66/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-21

## Components

- pypi · `academic-research-kernel`: 66/100 (this document), [markdown](https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel.md), [page](https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel)

## Channel facts

- Registry: `pypi`
- Package: `academic-research-kernel`
- Version: `2.0.0`
- Transport: `stdio`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-21.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it.
  - 1 of 20 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 32/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - License check failed: no license is declared.
  - Actively maintained (last published 0 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 73/100
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 1025 tokens (~85/item across 12 items; 12 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 98/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 95% of tool parameters carry a description.
- **Tool Safety**: 75/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "decision_ledger_validate" implies "execute" and declares no destructiveHint at all, which the MCP spec reads as destructive by default.
  - An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

**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.

## Install

### How do I install the Academic Research Kernel MCP server?

Academic Research Kernel runs locally as a PyPI package, launched with uvx academic-research-kernel. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add xngg1021-academic-research-kernel -- uvx academic-research-kernel
```

### Cursor

```json
{
  "mcpServers": {
    "xngg1021-academic-research-kernel": {
      "command": "uvx",
      "args": [
        "academic-research-kernel"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "xngg1021-academic-research-kernel": {
      "command": "uvx",
      "args": [
        "academic-research-kernel"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add xngg1021-academic-research-kernel -- uvx academic-research-kernel
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "xngg1021-academic-research-kernel": {
      "type": "local",
      "command": [
        "uvx",
        "academic-research-kernel"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add xngg1021-academic-research-kernel --command uvx --arg academic-research-kernel
```

### Hermes

```yaml
mcp_servers:
  xngg1021-academic-research-kernel:
    command: "uvx"
    args: ["academic-research-kernel"]
```

### Netclaw

```json
{
  "McpServers": {
    "xngg1021-academic-research-kernel": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "academic-research-kernel"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add xngg1021-academic-research-kernel -t stdio -c uvx -a academic-research-kernel
```

### Other

```json
{
  "mcpServers": {
    "xngg1021-academic-research-kernel": {
      "command": "uvx",
      "args": [
        "academic-research-kernel"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-21 (score 66)

First indexed and scored.

## MCP tools (12)

### `research_artifact_validate` (~104 tokens)

Validate a ResearchArtifactEnvelope against its schema and verify its cryptographic payload SHA-256 without side effects.

Input parameters:

- `bindings` (object)
- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `envelope` (object, required): The ResearchArtifactEnvelope dictionary to validate.
- `receipts` (object): Optional physical receipt registry required to replay receipt-backed CEG or Ledger snapshots.
- `state` (object)

### `research_artifact_ingest` (~140 tokens)

Deterministically ingest a scholarly artifact envelope into kernel state (ResearchObjects, CEG, and Ledger).

Input parameters:

- `bindings` (object): Optional explicit bindings (e.g. {'decision_id': '...', 'claim_id': '...'}).
- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `dry_run` (boolean): If true, simulates ingestion and returns plan without committing state.
- `envelope` (object, required): The ResearchArtifactEnvelope to ingest.
- `state` (object): Optional initial kernel state snapshot (containing 'ceg', 'ledger', 'objects').

### `research_receipt_verify` (~79 tokens)

Verify an AcademicEvidenceReceipt or LineageReceipt against a strongly typed ReceiptRef.

Input parameters:

- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `receipt_payload` (object, required): The full physical receipt payload dictionary to verify.
- `receipt_ref` (object, required): The ReceiptRef reference dictionary.

### `research_object_resolve` (~42 tokens)

Resolve and aggregate candidate bibliographic/identity records into discrete five-state equivalence judgments.

Input parameters:

- `records` (array, required): List of candidate metadata records sharing possible identifiers.

### `research_lineage_trace` (~56 tokens)

Trace backward provenance ancestry for a specified entity within a LineageGraph snapshot.

Input parameters:

- `lineage_graph` (object, required): Exported LineageGraph dictionary.
- `target_entity_id` (string, required): The entity ID to trace upstream lineage from.

### `claim_evidence_validate` (~84 tokens)

Validate the structural integrity, cycles, and cryptographic receipt bindings of a ClaimEvidenceGraph snapshot.

Input parameters:

- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `graph` (object, required): ClaimEvidenceGraph export dictionary.
- `receipts` (object): Physical receipt registry keyed by receipt ID or payload SHA-256.

### `claim_evidence_trace` (~94 tokens)

Trace all supporting and refuting evidence anchors and attached receipts for a given claim.

Input parameters:

- `claim_id` (string, required): Target claim identifier to trace.
- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `graph` (object, required): ClaimEvidenceGraph export dictionary.
- `receipts` (object): Physical receipt registry keyed by receipt ID or payload SHA-256.

### `decision_ledger_validate` (~77 tokens)

Execute complete four-gate verification over a Decision Ledger export dictionary.

Input parameters:

- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `ledger` (object, required): DecisionLedger export dictionary.
- `receipts` (object): Physical receipt registry keyed by receipt ID or payload SHA-256.

### `decision_trace` (~93 tokens)

Trace causal ancestry, basis dependencies, outcome corrections, and lifecycle state history for a decision.

Input parameters:

- `content_payloads` (object): Authorized base64 content by lineage entity ID; maximum total 10 MiB.
- `decision_id` (string, required): The decision identifier to trace.
- `ledger` (object, required): DecisionLedger export dictionary.
- `receipts` (object): Physical receipt registry keyed by receipt ID or payload SHA-256.

### `academic_recompute_statistics` (~151 tokens)

Recompute statistical claims (effect sizes, p-values, t-tests, CIs, OR/RR) to detect rounding errors or impossible figures.

Input parameters:

- `df` (number): Degrees of freedom (integer or Welch fractional)
- `mean1` (number): Group 1 mean
- `mean2` (number): Group 2 mean
- `n1` (integer): Group 1 size
- `n2` (integer): Group 2 size
- `p_value` (number): Reported p-value
- `sd1` (number): Group 1 SD
- `sd2` (number): Group 2 SD
- `t_stat` (number): Reported t-statistic

### `academic_check_percentage` (~71 tokens)

Check if a reported percentage and count can mathematically arise from a sample size.

Input parameters:

- `count` (integer, required): Observed count (numerator)
- `percent` (number, required): Reported percentage (e.g. 12.5)
- `sample_size` (integer): Optional total sample size (denominator)

### `academic_scfabric_hardware_probe` (~34 tokens)

Probe local hardware accelerators (CUDA, ROCm, MPS, XPU) and execution environments.

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel#diagnostics

## Score history

- 2026-09-21: 66

## Common questions

### What is the Academic Research Kernel MCP server?

Academic Research Kernel is an MCP server listed in the public MCP registry as io.github.xngg1021/academic-research-kernel. Deterministic research state, evidence validation and statistical recomputation. This page covers its PyPI package (academic-research-kernel).

### Is the Academic Research Kernel MCP server safe to use?

Academic Research Kernel scores 66 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. 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 Academic Research Kernel MCP server expose?

Academic Research Kernel exposes 12 tools: research_artifact_validate, research_artifact_ingest, research_receipt_verify, research_object_resolve, research_lineage_trace, and 7 more. Their descriptions and schemas cost roughly 1,025 tokens of context every time the server is loaded.

### Is the Academic Research Kernel MCP server still maintained?

Academic Research Kernel is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- PyPI project: https://pypi.org/project/academic-research-kernel/
- Socket report: https://socket.dev/pypi/package/academic-research-kernel
- Repository: https://github.com/xngg1021/academic-research-kernel
- Changelog RSS feed: https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel.xml
- Changelog JSON feed: https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel.json
- HTML version of this page: https://verifymcp.io/servers/xngg1021-academic-research-kernel/academic-research-kernel