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Metrx MCP Server

Metrx MCP Server

by metrxbots
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Get Dashboard Headline

metrx_get_dashboard_headline
Read-onlyIdempotent

Retrieve a dashboard headline with measured spend, estimated savings, and verified savings with confidence intervals for a 7-, 30-, or 90-day window; values include basis and provenance.

Instructions

The three-number headline regime: measured spend, estimated savings (the engine stored total, label Estimate; key attributed_savings) and verified savings-with-CI — each with basis, provenance, and tone. Values are micro-dollars (dollars = value / 1e6). Requires the optimization_model_v3 flag; while it is off this returns a clear next-step error. Response is envelope-shaped: every numeric leaf is a MeasuredValue {value, ci_low, ci_high, method, data_through}. ci fields are null unless the source payload carries a real confidence interval — never fabricated. Requires an API key with the 'read' scope.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "metrx_get_dashboard_headline"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoWindow in days — one of 7, 30, 90 (default 30)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.0

TDQS

A4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, closed-world), it discloses units (micro-dollars, divide by 1e6), the envelope-shaped response with every numeric leaf as MeasuredValue {value, ci_low, ci_high, method, data_through}, and the important guarantee that CI fields are null rather than fabricated. It also explains the feature-flag gating behavior and the auth scope, which are behavioral traits annotations cannot express.

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?

It is dense but well front-loaded: headline contents first, then units, then gating, then response shape, then auth. Some phrasing ('the three-number headline regime') is ornate, yet nearly every clause carries operational information an agent needs.

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

Completeness5/5

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

With no output schema, the description correctly takes on the burden of explaining the response envelope and the MeasuredValue leaf structure, including CI null semantics. Combined with the flag requirement and auth scope, an agent has everything needed to call this correctly and interpret the result.

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% and the single 'days' parameter already documents the 7/30/90 enum and default. The description adds no further meaning about the window parameter, so the schema does all the work here — baseline 3.

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 names a concrete resource and its contents: measured spend, estimated savings (key attributed_savings), and verified savings-with-CI, each with basis, provenance, and tone. That is far more specific than 'get dashboard headline,' though it never positions itself against sibling read tools such as metrx_get_cost_summary or metrx_get_balance.

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?

It gives real preconditions — the optimization_model_v3 flag must be on, and an API key with 'read' scope is required — plus what happens when the flag is off. What it does not provide is when to pick this tool over the many other read/reporting siblings, so usage selection is left implied.

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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