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Snowflake MCP Server

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Python
Remote

Find Tables

find_tables

Search across all Snowflake databases for tables by keyword, matching table names and comments to locate tables when you only know part of the name or purpose.

Instructions

Search for tables by keyword across ALL databases.

USE THIS WHEN: You don't know where a table is, but know part of its name or purpose.
Matches against both table names AND table comments (so a cryptically-named
table is still found when its comment mentions the term).

RETURNS (small result): flat list of matches
- [{database, schema, table, type, full_name}, ...]
  Note: neither the comment nor a column count is returned. The comment is the
  one unbounded field (can be a multi-KB doc-block); a column count does not
  help locate a table. For a table's comment and columns, use describe_table.

RETURNS (broad result): when too many tables match to return inline, the
COMPLETE result is written to a temp `.tsv` file and the response is instead a
compact summary built to help you NARROW: `total_hits`, `results_file`, a
bounded `top_groups` breakdown of the top database.schema clusters (with a
`(+X more groups, Y hits)` tail marker), and a `spilled` hint. To narrow, call
show_tables with database_pattern/schema_pattern from top_groups and/or a more
specific table_pattern -- don't blindly re-search.

HOW IT WORKS:
- Auto-refreshes cache if expired/empty (requires Snowflake auth on first use)
- Uses cached data if available (no auth needed)
- Searches table names and comments for the keyword (case-insensitive)

Parameters:
- search_term: Keyword to search for (case-insensitive)

Examples:
- find_tables("customer") - Find all customer-related tables across all databases
- find_tables("revenue") - Find revenue tables anywhere
- find_tables("staging") - Find tables with "staging" in name or comment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_termYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.3
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / search_term / title
      Removed value: -"Search Term"
  2. Changed1 schema field changedv1.0.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  3. First observedv0.2.3

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and delivers. It discloses cache auto-refresh, Snowflake auth on first use, cached-data fallback, case-insensitive search, and the exact small/broad return behaviors including the temp .tsv spill and the bounded summary fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but tightly organized with clear section headers (USE THIS WHEN, RETURNS, HOW IT WORKS, Examples). Every sentence carries operational value; examples are illustrative and the flow from purpose to usage to behavior is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter search tool with no output schema, the description fully specifies both return modes, auth requirements, narrowing strategies, and what is intentionally omitted (comments/column count). An agent can correctly invoke and interpret the tool without further guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 0%, the description compensates with a dedicated parameter explanation: 'search_term: Keyword to search for (case-insensitive)' plus three examples showing realistic usage. It also explains matching semantics against names and comments.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening line 'Search for tables by keyword across ALL databases' states a specific verb, resource, and scope. It further distinguishes the tool by noting it matches table names AND comments, which sets it apart from schema-based navigation tools like show_tables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'USE THIS WHEN' section explicitly defines the condition: 'You don't know where a table is, but know part of its name or purpose.' It also routes to siblings: 'use describe_table' for comments/columns and 'call show_tables with database_pattern/schema_pattern' to narrow broad results, with a warning not to blindly re-search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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