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alliance-genome

agr_postgres_mcp

Official

table_stats

Inspect PostgreSQL table statistics: live/dead tuples and last vacuum/analyze times to identify bloat and maintenance needs.

Instructions

Show table statistics including live/dead tuples, last vacuum/analyze times.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of tables to show (default: 20)
tableNoSpecific table name (optional, shows all if omitted)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the read-only nature ('Show') and lists specific statistics, which is useful. However, it does not mention any performance implications, default sorting/filtering behavior, or whether it only covers user tables versus system tables, leaving some ambiguity about the exact behavior.

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?

A single, front-loaded sentence with no filler. Every word adds value: it names the action, the resource, and two concrete statistics categories. This is an ideal level of conciseness for a simple tool.

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

Completeness4/5

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

The tool is simple, has two optional and well-documented parameters, and no output schema. The description adequately conveys the return content at a high level. The main gap is not explicitly stating that this is a safe read-only operation and not specifying the output format, but these are minor for a stats-display tool.

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 description coverage is 100%, so the schema already documents both 'limit' and 'table'. The description adds only a bit of context about the statistics returned, but no parameter-specific meaning beyond the schema. Baseline 3 is appropriate.

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 description states a specific verb ('Show'), a precise resource ('table statistics'), and the distinctive content ('live/dead tuples, last vacuum/analyze times'). This clearly distinguishes it from sibling tools like 'table_sizes' or 'row_counts' without needing to name them.

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 the many sibling tools, such as 'table_sizes', 'row_counts', or 'index_usage'. No context, prerequisites, or exclusions are mentioned, leaving the agent to infer usage solely from the name and one-line description.

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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