Borrowing it
Nothing to install: this file belongs to nsphung/mcp-snowflake-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nsphung/mcp-snowflake-server/main/.agents/skills/snowflake-integration-testing/SKILL.mdgit clone --depth 1 https://github.com/nsphung/mcp-snowflake-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/nsphung/mcp-snowflake-server/snowflake-integration-testing)<a href="https://agentmods.dev/skills/nsphung/mcp-snowflake-server/snowflake-integration-testing"><img src="https://agentmods.dev/badge/skills/nsphung/mcp-snowflake-server/snowflake-integration-testing/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/nsphung/mcp-snowflake-server/snowflake-integration-testing"><img src="https://agentmods.dev/badge/skills/nsphung/mcp-snowflake-server/snowflake-integration-testing.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 | $0.00079 | $0.00868 |
| Opus 5.5 | $0.00032 | $0.00347 |
| Sonnet 5.5 | $0.00016 | $0.00174 |
| Haiku 4.5 | $0.00008 | $0.00087 |
Grade A, and why
snowflake-integration-testing 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 2mo 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Snowflake Integration Testing
When to Use
- Verifying MCP Snowflake server connectivity after configuration changes
- Testing that MCP tools (
list_databases,list_schemas,list_tables,describe_table,read_query) work correctly - Exploring Snowflake schemas to validate data access permissions
- Discovering tables with specific column data types (VARIANT, OBJECT, ARRAY)
- Sampling rows to verify data shape, nested JSON structure, and content
- Validating Docker or local MCP server deployments
Available MCP Tools
| Tool | Purpose |
|---|---|
list_databases |
List all accessible databases |
list_schemas |
List schemas in a database |
list_tables |
List tables in a database.schema |
describe_table |
Get column metadata for a fully-qualified table |
read_query |
Execute a read-only SQL query |
Procedure
1. Connectivity Check
Verify the connection is active and inspect the session context:
SELECT CURRENT_USER(), CURRENT_ROLE(), CURRENT_WAREHOUSE(), CURRENT_DATABASE(), CURRENT_SCHEMA()
Use read_query to execute this. Confirm user, role, warehouse, database, and schema are as expected.
2. Schema Exploration
Follow this sequence to explore the data landscape:
- Call
list_databases— note accessible databases - Pick a target database, call
list_schemaswithdatabaseparameter - Pick a target schema, call
list_tableswithdatabaseandschemaparameters - For specific tables, call
describe_tablewith fully-qualified name (database.schema.table)
3. Data Type Discovery
Find tables with specific column types using read_query:
-- Find OBJECT columns (nested JSON)
SELECT table_schema, table_name, column_name, data_type
FROM <DATABASE>.information_schema.columns
WHERE data_type = 'OBJECT'
LIMIT 20
-- Find VARIANT columns
SELECT table_schema, table_name, column_name, data_type
FROM <DATABASE>.information_schema.columns
WHERE data_type = 'VARIANT'
LIMIT 20
-- Find ARRAY columns
SELECT table_schema, table_name, column_name, data_type
FROM <DATABASE>.information_schema.columns
WHERE data_type = 'ARRAY'
LIMIT 20
What ships with it
2 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.
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.
- 2mo ago First seen · 110 lines · 79 tokens per session scan A 991a9b75172f
snowflake-integration-testing is a skill published in the GitHub repository nsphung/mcp-snowflake-server (2 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 868 once invoked, about $0.0003 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 2026-08-31.
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