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MCP Vibe Coding Tools

MCP Vibe Coding Tools

by ammilam
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TypeScript
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gcloud_bq_ls

List BigQuery datasets or tables in a project. Specify a dataset to view its tables, or omit it to see all datasets. Limit results with max_results.

Instructions

List BigQuery datasets or tables

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": "gcloud_bq_ls"
  }
}

Input Schema

NameRequiredDescriptionDefault
datasetNoDataset name to list tables from (if not provided, lists datasets)
projectNoProject ID (uses current project if not specified)
max_resultsNoMaximum number of results to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description must cover behavioral aspects. It states the tool lists datasets or tables, which implies a read-only operation, but does not disclose any side effects, authorization needs, or other behavioral traits. The absence of annotations raises the burden somewhat, but the description is not misleading or contradictory.

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

Conciseness4/5

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

The description is very short (6 words) and front-loads the core purpose. It is efficient with no wasted words. However, it could be slightly more structured or include brief context without increasing verbosity significantly.

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?

For a simple listing tool with 3 well-documented parameters and no output schema, the description is minimal but sufficient. It conveys the primary function. However, it omits details like result format or pagination behavior, which could be helpful. The presence of siblings like gcloud_bq_show and gcloud_bq_mk provides some context, but the description itself is not fully complete.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description does not add any information about parameters beyond what the schema already provides in their descriptions. The parameter descriptions in the schema are clear, so the tool description contributes no extra semantic value.

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?

The description 'List BigQuery datasets or tables' clearly states the verb (list) and resource (BigQuery datasets or tables). It distinguishes the tool from siblings like gcloud_bq_query and gcloud_bq_show, as it focuses on listing rather than querying or showing details. However, lacking a title and more specificity on the listing scope slightly reduces clarity.

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 usage for listing datasets or tables, but provides no explicit guidance on when to use this tool versus alternatives like gcloud_storage_buckets_list or gcloud_bq_show. There are no 'when to use' or 'when not to use' statements, making the agent rely on the tool name and basic purpose. The schema clarifies that omitting 'dataset' lists datasets, which serves as implicit usage context.

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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