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Berth

Berth

Official
by seayniclabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

db_active_queries

Monitor currently running PostgreSQL queries to identify performance bottlenecks and manage database connections effectively.

Instructions

Currently running queries (PostgreSQL only — pg_stat_activity).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "connection_id": "<connection_id>"
    },
    "name": "db_active_queries"
  }
}

Input Schema

NameRequiredDescriptionDefault
connection_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data source (pg_stat_activity) but doesn't explain what 'currently running' means (e.g., real-time vs cached), whether this is read-only or has side effects, or any permissions/rate limits. This is inadequate for a tool that likely accesses system-level data.

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 extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place, with no wasted text or redundancy, making it easy to parse quickly.

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?

Given the tool's moderate complexity (accessing system queries), no annotations, and an output schema (which reduces need to describe returns), the description is minimally adequate. It identifies the data source but lacks details on behavior, parameters, and usage context, leaving significant gaps for effective tool selection.

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

Parameters2/5

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

The schema description coverage is 0%, so the description must compensate for the undocumented parameter. It adds no information about the 'connection_id' parameter—what it represents, how to obtain it, or its format. This leaves a critical gap for the single required parameter.

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 clearly states the tool's purpose: retrieving currently running queries from PostgreSQL's pg_stat_activity. It specifies the verb ('running queries') and resource ('PostgreSQL only'), but doesn't explicitly differentiate from siblings like db_query or db_execute, which might also involve query operations.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an active connection), exclusions, or comparisons to siblings like db_query (for executing queries) or db_execute (for running commands).

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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