Nemo Retriever MCP
NVIDIAOfficialPublished by NVIDIA
- Skill
- Tool integration
- Automation and integrations
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The NeMo Retriever MCP skill provides a structured interface for interacting with the NeMo Retriever service via the Model Context Protocol (MCP). It enables agents to perform semantic searches across indexed documents and ingest new content when necessary. The skill emphasizes using specific tool calls for querying and ingestion while ensuring that agents properly ground their responses in the retrieved evidence, including maintaining document identifiers for accurate citations.
What it does
- Provides access to NeMo Retriever MCP tools for document retrieval and ingestion.
- Supports querying existing document indices to find relevant information.
- Facilitates the ingestion of new documents into the retriever system.
- Guides the agent to ground answers using retrieved evidence hits.
- Ensures document metadata and identifiers are preserved for citation purposes.
When to use it
- When an agent needs to search through a collection of documents to answer a user query.
- When new documents must be added to the retriever index for future searchability.
- When the task requires evidence-based responses with specific source citations.
- When working within an environment that supports NeMo Retriever MCP server tools.
Advantages
- Standardizes the interaction pattern for document retrieval tasks.
- Promotes accurate grounding by requiring evidence hits to be read and cited.
- Prevents redundant ingestion of already searchable documents.
- Provides clear error handling and retry guidance for query operations.
Usage examples
- “Search for information regarding the company's Q3 policy in the retriever.”
- The agent uses the query tool with a search string, retrieves the top 5 hits, and synthesizes an answer citing the specific document IDs and page numbers provided.
- “Ingest the document at /shared/data/policy.pdf and then find the section on remote work.”
- The agent calls ingest_documents for the specified path, confirms success, and then performs a query to extract the requested information about remote work.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/nemo-retriever-mcp/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nemo-retriever-mcp.zip -d .agents/skills
Documentation by NVIDIA
NeMo Retriever MCP
Use the NeMo Retriever MCP tools exposed under retriever instead
of the retriever CLI.
The server exposes MCP tools, not MCP resources. Do not call
list_mcp_resources to discover documents or decide whether Retriever is
available; an empty MCP resource list is expected.
Workflow
Use
queryto search documents already available to Retriever.Never ingest documents that are already searchable, including documents in a prebuilt index.
Use
ingest_documentsonly when required documents are not yet searchable and server-readable paths are explicitly available. Do not assume that paths visible to the agent are also readable by the Retriever server.After ingestion completes, use
queryto retrieve evidence from the added documents.Start with
top_k=5andformat="hits". Request more only when the first successful response does not contain enough evidence to answer the task.Read evidence hits from the tool response and ground your answer in them.
Preserve available document identifiers such as
document_id,source_id, orpdf_basename, along with page numbers, when the task requests citations or structured output.If
queryfails, inspect the error and retry with corrected parameters or a shorter equivalent query. If it still fails, report the error before using another retrieval method.
Files
- ATTRIBUTION.mdAdded by AutoGineer835 B
- BENCHMARK.md7.5 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md1.5 KiB
- evals/evals.json4.4 KiB
- skill-card.md3.9 KiB
- skill.oms.sig4.5 KiB
BENCHMARK.md
Skill Benchmark: nemo-retriever-mcp
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
nemo-retriever-mcpEvaluation date: 2026-09-12
Evaluator version:
1.5.6Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)Tasks: 4 evaluation tasks (3 positive, 1 negative)
Dataset digest:
sha256:66c211f3143896abe07a31fd20994bea25e2de26ead9f3ede9af351fcbc969e5(skill-evaluator-dataset-snapshot/1)Attempts per task: 3
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
What This Report Answers
The three-tier evaluation checks whether the skill:
is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.
Results at a Glance
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 77.1% — baseline ran, but no comparable score was available; uplift unavailable | 69.2% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 93.8% → 100.0% (+6.2 points) | 30.0% → 90.0% (+60.0 points) |
| Correctness | 17.5% → 60.0% (+42.5 points) | 16.0% → 44.0% (+28.0 points) |
| Discoverability | 88.3% — baseline ran, but no comparable score was available; uplift unavailable | 75.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 39.3% → 50.6% (+11.3 points) | 28.5% → 44.3% (+15.8 points) |
| Efficiency | 86.4% — baseline ran, but no comparable score was available; uplift unavailable | 92.9% — baseline ran, but no comparable score was available; uplift unavailable |
How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 970,886 | 1,055,581 | N/A | N/A | skill 4/4; base 8/8 |
| claude-code | nemo-retriever-mcp-001 | 120,998 | 272,278 | N/A | N/A | skill 1/1; base 3/3 |
| claude-code | nemo-retriever-mcp-002 | 395,568 | 597,886 | N/A | N/A | skill 1/1; base 3/3 |
| claude-code | nemo-retriever-mcp-003 | 424,583 | 155,744 | +268,839 | +172.62% | skill 1/1; base 1/1 |
| claude-code | nemo-retriever-mcp-004 | 29,737 | 29,673 | +64 | +0.22% | skill 1/1; base 1/1 |
| codex | All cases | 1,296,562 | 5,600,373 | N/A | N/A | skill 5/5; base 10/10 |
| codex | nemo-retriever-mcp-001 | 169,544 | 551,996 | N/A | N/A | skill 1/1; base 3/3 |
| codex | nemo-retriever-mcp-002 | 881,093 | 1,542,171 | N/A | N/A | skill 2/2; base 3/3 |
| codex | nemo-retriever-mcp-003 | 232,325 | 3,492,642 | N/A | N/A | skill 1/1; base 3/3 |
| codex | nemo-retriever-mcp-004 | 13,600 | 13,564 | +36 | +0.27% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 2,267,448 | 6,655,954 | N/A | N/A | skill 9/9; base 18/18 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 14 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 4 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.author' (
skills/nemo-retriever-mcp/SKILL.md)MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (
skills/nemo-retriever-mcp/SKILL.md)MEDIUM QUALITY/quality_reliability: MCP skill lacks connection/error guidance (
skills/nemo-retriever-mcp/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (
skills/nemo-retriever-mcp/SKILL.md)MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (
skills/nemo-retriever-mcp/SKILL.md)9 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did it avoid wasted tool calls and token usage? | skill_efficiency (50%) + token_efficiency (50%) |
Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (
goal_accuracy) and expected workflow adherence (behavior_check).Efficiency is 50% tool-call productivity (the backward-compatible
skill_efficiencywire id) and 50%token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
Signals present in this run:
security(Security): unsafe operations, secret leakage, and unauthorized access.skill_execution(Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.skill_efficiency(Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).accuracy(Accuracy): final-answer correctness against the reference answer.goal_accuracy(Goal Accuracy): whether the user's goal was achieved.behavior_check(Behavior Check): whether the expected workflow behavior was followed.token_efficiency(Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
</details>
Freshness
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/nemo-retriever-mcp · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-retriever-mcp.zip