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io.github.nipunkhanderia/golden-dataset-mcp

PYPI · GOLDEN-DATASET-MCP · SCANNED SEP 21

Version-controlled golden datasets and RAG evaluation, no API key needed.

Available components

0 this week 75 Trust /100
Trust breakdown (7 categories)

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 Security100
  • No malware found by supply-chain analysis.Pass
  • No known CVEs affecting this package version or its production dependencies.Pass
  • Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
  • 1 of 24 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency32
Schema Quality & AI Usability70
  • AI-judged instruction clarity (good).Pass
  • Tool/resource definitions use about 552 tokens (~61/item across 9 items; 9 tools + 0 resources), lean.Pass
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management87
  • Stability observed for 26 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage71
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 0% of tool parameters carry a description.Fail
  • Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "delete_entry" implies "delete" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
  • 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 current MCP spec version (2026-07-28).Pass
Install

How do I install the io.github.nipunkhanderia/golden-dataset-mcp server?

io.github.nipunkhanderia/golden-dataset-mcp runs locally as a PyPI package, launched with uvx golden-dataset-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

pypi · golden-dataset-mcp

# add to Claude Code
claude mcp add nipunkhanderia-golden-dataset-mcp -- uvx golden-dataset-mcp
// .cursor/mcp.json
{
  "mcpServers": {
    "nipunkhanderia-golden-dataset-mcp": {
      "command": "uvx",
      "args": [
        "golden-dataset-mcp"
      ]
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "nipunkhanderia-golden-dataset-mcp": {
      "command": "uvx",
      "args": [
        "golden-dataset-mcp"
      ]
    }
  }
}
# add to Codex CLI
codex mcp add nipunkhanderia-golden-dataset-mcp -- uvx golden-dataset-mcp
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "nipunkhanderia-golden-dataset-mcp": {
      "type": "local",
      "command": [
        "uvx",
        "golden-dataset-mcp"
      ],
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add nipunkhanderia-golden-dataset-mcp --command uvx --arg golden-dataset-mcp
# ~/.hermes/config.yaml
mcp_servers:
  nipunkhanderia-golden-dataset-mcp:
    command: "uvx"
    args: ["golden-dataset-mcp"]
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "nipunkhanderia-golden-dataset-mcp": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "golden-dataset-mcp"
      ]
    }
  }
}
# add to Vellum
assistant mcp add nipunkhanderia-golden-dataset-mcp -t stdio -c uvx -a golden-dataset-mcp
// mcp.json
{
  "mcpServers": {
    "nipunkhanderia-golden-dataset-mcp": {
      "command": "uvx",
      "args": [
        "golden-dataset-mcp"
      ]
    }
  }
}
Changelog

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.

  • 20 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.

  • 19 Sept 26 −3
    • Stability: pass → 0.80 functional
  • 18 Sept 26 0
    • Stability: 0.97 → pass security
  • 17 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.

  • 15 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.

  • 13 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.

  • 12 Sept 26 −3
    • Stability: pass → 0.80 functional
  • 11 Sept 26 0
    • Stability: 0.97 → pass security
Diagnostics

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 21 Sept 2026 · Analysed pypi/golden-dataset-mcp@0.1.2

Provenance No attestation

The registry publishes no build provenance for this version, so there is nothing to verify.

Result No attestation
Ecosystem pypi

Background: How many MCP packages publish verified provenance →

Install scripts 1 script
Hook Tier Command
build_backend allowlisted hatchling.build

Background: Why install scripts are a supply-chain risk →

Dependencies 24 packages
Packages resolved 24
Stale 1
Tree resolution Complete

Background: SBOMs and build attestations, explained →

MCP tools · 9 exposed · ~450 tokens

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 →

Tool Tokens
add_entry ~54

Add a question-answer pair to the working tree of a golden dataset. Entries added here are NOT yet versioned — call commit_version to snapshot them. dataset_path must already be initialised.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
idstringyes
statusstringyes

No examples provided.

commit_version ~56

Snapshot the current working tree as a new immutable dataset version. Versions auto-increment (1.0 -> 1.1 -> 1.2...). Fails if the working tree is empty.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
entry_countintegeryes
parent_versionyes
sha256stringyes
versionstringyes

No examples provided.

dataset_status ~34

Show the current state of a golden dataset: name, current version, and working tree size.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
current_versionyes
descriptionstringyes
namestringyes
versionsarrayyes
working_tree_entry_countintegeryes

No examples provided.

delete_entry ~35

Remove an entry from the working tree by its id. Does not affect already-committed versions.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
idstringyes
statusstringyes

No examples provided.

diff_versions ~28

Show entries added, removed, or changed between two committed versions.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
addedarrayyes
changedarrayyes
removedarrayyes

No examples provided.

evaluate_answers ~78

Score actual LLM/RAG-generated answers against the golden dataset using TF-IDF cosine similarity (no LLM call, no API key needed). actual_answers must be supplied in the same order as the entries in the target version. Omit `version` to evaluate against the current committed version.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
avg_semantic_similarityyes
dataset_namestringyes
passedbooleanyes
resultsarrayyes
total_entriesintegeryes
versionstringyes

No examples provided.

init_dataset ~58

Initialise a new version-controlled golden dataset at dataset_path. Creates a .golden_dataset/ directory there. Fails if one already exists at that path — delete .golden_dataset/ manually to start fresh.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
created_atstringyes
descriptionstringyes
namestringyes

No examples provided.

list_entries ~52

List entries in a dataset. Omit version to see the uncommitted working tree; pass a version (e.g. '1.0') to see a committed snapshot.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
countintegeryes
entriesarrayyes

No examples provided.

update_entry ~55

Update fields of an existing working-tree entry by its id. Only fields you provide are changed; omitted fields are left as-is. Raises an error if entry_id is not found in the working tree.

NameTypeReqDescription
inputobjectyes
NameTypeReqDescription
answerstringyes
contextsarrayyes
idstringyes
metadataobjectyes
questionstringyes
tagsarrayyes
updated_atstringyes

No examples provided.

Common questions

What is the io.github.nipunkhanderia/golden-dataset-mcp server?

io.github.nipunkhanderia/golden-dataset-mcp is listed in the public MCP registry as io.github.nipunkhanderia/golden-dataset-mcp. Version-controlled golden datasets and RAG evaluation, no API key needed. This page covers its PyPI package (golden-dataset-mcp).

Is the io.github.nipunkhanderia/golden-dataset-mcp server safe to use?

io.github.nipunkhanderia/golden-dataset-mcp scores 75 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 io.github.nipunkhanderia/golden-dataset-mcp server expose?

io.github.nipunkhanderia/golden-dataset-mcp exposes 9 tools: init_dataset, add_entry, update_entry, delete_entry, list_entries, and 4 more. Their descriptions and schemas cost roughly 450 tokens of context every time the server is loaded.

Is the io.github.nipunkhanderia/golden-dataset-mcp server still maintained?

io.github.nipunkhanderia/golden-dataset-mcp 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.