Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Friz-zy/ai-capability-registry --skill amazon-kendra-indexgit clone --depth 1 https://github.com/Friz-zy/ai-capability-registryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/friz-zy/ai-capability-registry/amazon-kendra-index)<a href="https://agentmods.dev/skills/friz-zy/ai-capability-registry/amazon-kendra-index"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/amazon-kendra-index/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/friz-zy/ai-capability-registry/amazon-kendra-index"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/amazon-kendra-index.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.00309 |
| Opus 5 | $0.00007 | $0.00154 |
| Sonnet 5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
amazon-kendra-index-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Amazon Kendra Index
Enterprise search and RAG enhancement.
When to use
- Use Amazon Kendra Index only when the task directly involves the relevant service, SaaS product, platform, or technology.
Connection
Docker stdio
{
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"mcp/amazon-kendra-index-mcp-server"
]
}
MCP instructions
Docker launch notes
- Launch through Docker stdio with
docker run --rm -i mcp/amazon-kendra-index-mcp-server.
References
- https://github.com/awslabs/mcp
- https://github.com/docker/mcp-registry/tree/main/servers/amazon-kendra-index
Security policy
- Trust:
reviewed - Default mode:
manual_review - Permission default:
manual_review - Authentication:
Unspecified in source metadata - Warning: Default mode is
manual_review. - Warning: Permission default is
manual_review. - Required posture: Complete manual review before connecting or invoking tools; this generated record does not grant approval.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 61 lines · 14 tokens per session scan A 90e53cef45b4
amazon-kendra-index-mcp is a skill published in the GitHub repository Friz-zy/ai-capability-registry (9 stars, last pushed 4d ago), licensed MIT. It adds 14 tokens to every session and 309 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
senior-ml-engineer
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models…
bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
rag-patterns
RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop. Triggers: RAG, embedding, pgvector, Qdrant, Pinecone, Weaviate, reranker, semantic search.
evaluate
Evaluates RAG retrieval and LLM-as-judge metrics (faithfulness, relevancy, context precision). Triggers: measure RAG quality, knowledge gap, RAG eval, golden dataset.