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mcp-kpi-server

mcp-kpi-server

by poojabhather99
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_kpi

Computes named KPIs such as completion rate, average duration, or visits by district from the analytical database. Use it to retrieve specific business metrics by name.

Instructions

Compute a named KPI: completion_rate, avg_duration_min or visits_by_district.

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": {
      "name": "<name>"
    },
    "name": "get_kpi"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden, and it does not say whether the tool is read-only, what shape the result takes (scalar vs. row vs. table), or whether the KPI is computed over the whole dataset or a period. 'Compute' hints at a read but nothing is confirmed.

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?

A single tight sentence with the enumerations front-loaded and no filler. It is efficient but arguably too sparse for a tool with no annotations and no output schema.

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

Completeness2/5

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

With no annotations, no output schema, and 0% schema coverage, the description must cover behavior and return values, and it does neither. An agent cannot tell whether the result is a number or a result set, or over what population the KPI is aggregated.

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 coverage is 0% and the schema itself declares no enum, so the description is the only place the three legal values for 'name' appear. That is genuine semantic compensation, though it omits error behavior for invalid names and any time-window semantics.

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 gives a specific verb ('Compute') and a specific resource ('a named KPI') and enumerates the three valid KPI names, so the agent knows exactly what will happen. It does not explicitly differentiate itself from the generic sibling run_select, but the enumeration makes the specialization clear.

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?

Listing the three allowed names implicitly scopes usage, but the description never states when to prefer this over run_select or list_tables/describe_table, nor what happens if the name is not in the list. Usage is inferable rather than stated.

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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