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@qybbs/custom-bigquery-mcp

@qybbs/custom-bigquery-mcp

by qybbs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

list_allowed_tables

Lists all BigQuery tables permitted for query execution under the server's allowlist, helping you identify safe, readable datasets before running queries.

Instructions

List all BigQuery tables allowed for query execution.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_allowed_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It states the resource but omits behavioral traits: whether results are paginated or capped, whether this requires BigQuery auth, and what the listing actually returns (names only vs. metadata). For a no-annotation tool this is a substantial gap.

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?

One front-loaded sentence with the resource and scope stated immediately and zero wasted words. Nothing could be trimmed without losing meaning.

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

Completeness3/5

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

The tool is simple (no params, no output schema, no annotations), and the description covers what is listed and the permission scope. However, with no output schema it should indicate the shape of the return (e.g., table identifiers) and any result-size caveat, which it does not.

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

Parameters4/5

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

The tool takes zero parameters, which per the rubric sets a baseline of 4; there are no parameter semantics to add beyond what the empty schema already conveys.

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

Purpose4/5

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

States a specific verb (List) and resource (BigQuery tables) with a clear scope qualifier ('allowed for query execution'). It is distinguishable from describe_table and execute_readonly_query, but it does not explicitly contrast with the sibling search_allowed_tables, so differentiation is inferential rather than stated.

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

Usage Guidelines3/5

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

The description implies this is the enumeration tool for discovering permitted tables, but it never says when to use it versus search_allowed_tables (search vs. list-all is left to inference) and gives no exclusions or prerequisites.

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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