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P2predict-mcp

by ahmed-khalil-hafsi
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predict_interval

Get a likely range for a part's price using conformal prediction. Use the band width to judge trust—tight means predict with confidence, wide means request a quote.

Instructions

Predict with a likely range (conformal prediction interval).

For a 90% interval, about 9 in 10 similar parts fall within the range.
coverage is an integer 1-99 (default 90). Requires a model trained with
P2Predict v0.5+ (which stores calibration data).

Reading it for the user: the band WIDTH is the per-part trust signal, and
the payload computes it for you — `interval.reliability`
('trust' | 'caution' | 'quote') and a plain `interval.say_to_user` sentence
you can quote directly. A tight band = predict with confidence; a very wide
band — or a lower bound at/below $0 on an additive (non-log) model — means
"get a quote, don't benchmark." Always surface the range, not just the point
estimate, when the user will act on the number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coverageNo
featuresYes
model_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.1

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the interval reliability output ('trust' | 'caution' | 'quote'), the meaning of tight versus wide bands, the lower-bound-at-$0 caveat on non-log models, and that the payload provides a ready-to-quote sentence. This is unusually transparent about expected behavior and interpretation.

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?

The description is front-loaded with the core purpose and then organized into calibration explanation and user-facing reading instructions. Every section adds practical value, though the second half is fairly long and could be tightened without losing meaning.

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?

The description covers the tool's purpose, a prerequisite, the output interpretation, and actionable guidance for when to trust the prediction. The main completeness gap is the underspecified 'features' parameter, but the output semantics are thoroughly explained even though an output schema exists.

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 0%, so the description must compensate. It does add meaningful semantics for the 'coverage' parameter (integer 1-99, default 90, and what it means probabilistically) and implies the model_id prerequisite. However, the 'features' object—the other required parameter—remains completely opaque; the description does not say what keys or shape are expected.

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 opens with a specific verb and resource: 'Predict with a likely range (conformal prediction interval).' It clearly distinguishes this from a point prediction (e.g., the sibling 'predict') by defining the interval semantics and even explains what a 90% interval means.

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 gives strong contextual guidance: it requires a model trained with P2Predict v0.5+ and explicitly tells the agent when to surface the range rather than just the point estimate ('when the user will act on the number'). It does not explicitly name alternatives like 'predict' or state when not to use this tool, so it stops short of a 5.

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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