estimate-maestro

by Odalrick

Overview Schema Source Related Servers Discussions Admin

Python

Local

summary

Summarize calibration data to measure estimation accuracy, or decline when data is insufficient.

Instructions

Aggregate calibration, or an honest refusal when there is too little data.

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": "summary"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`note`	Yes
`efforts_counted`	Yes
`mean_hours_per_point`	Yes
`mean_inventory_growth`	Yes
`median_hours_per_point`	Yes
`median_inventory_growth`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 27, 2026

TDQS

B3.1/5.0

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 does disclose one behavioral trait beyond the obvious: an 'honest refusal when there is too little data,' which signals the tool can return a refusal rather than a fabricated aggregate. But nothing else about behavior is stated — no mention of whether the aggregation is computed on-demand or cached, what states of calibration data trigger the refusal threshold, or what the refused/aggregate outputs look like.

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, front-loaded sentence with no filler words. It earns its place but trades away critical clarity for brevity — the 'calibration' term is never unpacked, and the refusal condition is compressed into a subordinate clause. Economical, but arguably under-specified in service of economy.

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?

Mitigating factors: an output schema exists (though not shown) explaining return values, and zero parameters simplify the call contract. Nevertheless, the description leaves the core noun undefined — what 'calibration' aggregates — and never outlines the threshold for 'too little data.' For a tool that is entirely about its output semantics, this is a meaningful gap.

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?

Zero parameters, so the baseline is 4 and there is nothing for the description to clarify. The one relevant semantic — the tool operates on existing calibration state rather than requiring arguments — is implicitly conveyed by the empty schema, and the refusal phrase adds the data-sufficiency condition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a verb ('aggregate') and a resource ('calibration'), so it clears the tautology bar. However, 'calibration' is a domain-specific term never explained — in a family of time-tracking/effort tools (clock_in, record_revision, effort_table), it's unclear whether calibration means planned-vs-actual comparison, summary statistics, or something else. The agent can guess but can't be sure.

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?

No guidance on when to call this versus any sibling. None of the 19 sibling tools are referenced, and no condition distinguishing 'aggregate calibration' from similar summarization operations is given. An agent facing siblings like effort_table and effort_detail has no basis for choosing summary over them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.