IBM Data Intelligence MCP Server is a Model Context Protocol server that acts as a shared gateway for making tools available across microservices in the Watsonx Data Intelligence ecosystem. It connects MCP clients with IBM data-intelligence services and supports clients such as Claude, Watsonx Orchestrate, and IBM Bob. The catalogue lists five skills for working with the server.
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 IBM/data-intelligence-mcp-server --skill data-product-creationgit clone --depth 1 https://github.com/IBM/data-intelligence-mcp-serverWrote 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/ibm/data-intelligence-mcp-server/data-product-creation)<a href="https://agentmods.dev/skills/ibm/data-intelligence-mcp-server/data-product-creation"><img src="https://agentmods.dev/badge/skills/ibm/data-intelligence-mcp-server/data-product-creation/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/ibm/data-intelligence-mcp-server/data-product-creation"><img src="https://agentmods.dev/badge/skills/ibm/data-intelligence-mcp-server/data-product-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00153 | $0.06033 |
| Opus 5.5 | $0.00061 | $0.02413 |
| Sonnet 5.5 | $0.00031 | $0.01207 |
| Haiku 4.5 | $0.00015 | $0.00603 |
Grade A, and why
data-product-creation 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 810 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Product Creation Skill (File-Based Specification)
You are helping a user create and publish a high-quality data product in IBM watsonx.data intelligence (Data Product Hub) using an Infrastructure-as-Code approach. Instead of making iterative API calls, you will generate specification files that define the data product, allow the user to review and refine them, then batch-submit to DPH.
Core Paradigm: File-Based Specification
File-Based Approach (Use This):
Human ↔ LLM ↔ Workspace Files → MCP Tools (5-7 batch API calls)
Benefits:
- Version Control: Files can be committed to Git, tracked, branched, and merged
- Collaboration: Multiple stakeholders can review and edit specification files
- Reusability: Templates can be created for common data product patterns
- Validation: Files can be validated before any API calls are made
- Transparency: Complete data product definition visible in one place
- Iteration: Refine specifications without touching the DPH server
Workflow Overview
This skill has five phases. Follow them in order:
- Discovery & Requirements (conversational + MCP tools)
- Specification Generation (create files in workspace)
- Iterative Refinement (user reviews/edits files)
- Validation (AI code review of files)
- Publication (batch MCP tool calls)
Optional Calls
WHEN: User asks about available data contracts (e.g., "What data contracts are available?")
CALL: list_data_product_contract_templates tool
WHEN: User asks about available business domains (e.g., "What domains are available?")
CALL: list_data_product_business_domains tool
WHEN: User asks about available delivery methods for a data asset (e.g., "What delivery methods are available for asset_name?")
CALL: list_data_product_delivery_methods tool
Phase 1 — Discovery & Requirements
Goal: Understand what the user wants to create and check for duplicates.
1.1 Deduplication Check
What ships with it
8 files 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.
- templates/assets_manifest_asset_based.json 639 B
- templates/assets_manifest_url_based.json 248 B
- templates/contract_custom.json 1.9 KB
- templates/contract_template.json 213 B
- templates/contract_url.json 127 B
- templates/data_product_spec.json 342 B
- templates/delivery_config.json 340 B
- templates/README.md 2.8 KB
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.
- 1mo ago Changed 737525e18364
- 1mo ago First seen · 810 lines · 153 tokens per session scan A 29fcfe163f71
data-product-creation is a skill published in the GitHub repository IBM/data-intelligence-mcp-server (20 stars, last pushed 2d ago), licensed Apache-2.0. It adds 153 tokens to every session and 6,033 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-08-30.
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