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mcp-kpi-server

mcp-kpi-server

by poojabhather99
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

run_select

Run a single read-only SELECT query against the analytical DuckDB database, returning up to 200 rows for safe data inspection.

Instructions

Run a single read-only SELECT query (max 200 rows returned).

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": {
      "sql": "<sql>"
    },
    "name": "run_select"
  }
}

Input Schema

NameRequiredDescriptionDefault
sqlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose the two most important traits — read-only safety and a hard 200-row cap. It omits what happens at the cap (silent truncation vs error), dialect/driver expectations, permissions required, and timeout 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?

One sentence, front-loaded with the operation and immediately followed by the key constraint. Nothing is wasted.

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 one-parameter tool with no annotations and no output schema, the essentials are present, but an agent still cannot predict row-cap behavior, error modes, or dialect — gaps that matter for a SQL execution 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?

The single 'sql' parameter has 0% schema description coverage, so the description must compensate. It does add real meaning — the statement must be a SELECT, read-only, and singular — but says nothing about dialect, semicolons, or whether LIMIT/aggregates are permitted.

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?

Names a specific verb and resource ('Run a single read-only SELECT query') and adds the row-cap constraint, which makes the tool's role as a data-retrieval endpoint clear. It never names the metadata siblings (list_tables, describe_table, get_kpi), so differentiation is inferred from the domain 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?

Usage is only implied: 'SELECT query' plus 'max 200 rows' signals exploratory/preview data retrieval, and 'read-only' implicitly excludes writes. There is no explicit statement of when to reach for this versus list_tables or describe_table, and no guidance on what to do if more than 200 rows are needed.

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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