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nina-mcp

nina-mcp

by anarcoiris
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ts_read_table

Read rows from a Target Scheduler database table with optional filtering by column value and row limit.

Instructions

Read rows from a Target Scheduler database table.

where_column / where_value: optionally filter to rows where this column equals this value. limit: max rows to return (capped at 2000).

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": {
      "table": "<table>"
    },
    "name": "ts_read_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
tableYes
where_valueNo
where_columnNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the limit cap (2000) and optional filtering, implying read-only behavior. It could be more explicit about return behavior for missing tables, but overall transparent.

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: two brief sentences front-loading the primary purpose, followed by parameter explanations. No wasted words.

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?

Given the presence of an output schema, the description focuses on input parameters adequately. It covers the main aspects but could mention error handling or that only equality filtering is supported.

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?

Schema description coverage is 0%, so the description must compensate. It explains the where_column/where_value pair for filtering and the limit parameter with a cap. The table parameter is not described but its purpose is obvious from context.

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 clearly states the verb 'Read rows' and the resource 'Target Scheduler database table'. It distinguishes from sibling tools like ts_describe_table and ts_list_tables by specifying row-level reading.

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

Usage Guidelines4/5

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

The description explains optional filtering and a limit cap, which helps the agent understand usage context. However, it does not explicitly state when not to use or mention alternative tools.

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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