P2predict-mcp

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list_models

Discover which trained models are available for price benchmarking, showing each model's target and a clear summary. Use this to select the right model for your query.

Instructions

List all trained P2Predict models in the configured models directory.

Call this first to discover which models are available. Each model carries
a plain `say_to_user` line, its target, and its specs. Lead with
`say_to_user`; do NOT read out raw fields like algorithm or R² (and never
the words 'SHAP', 'log-target', 'R²') to a category manager. Pass
include_internal=true only when you need the algorithm name / R² / log-target
flag for your own reasoning.

The response also carries a `server` block (version + git short-SHA + source
path) identifying the build this server process loaded — useful to confirm a
code change actually took effect (MCP servers are long-lived; a stale process
serves old code until restarted).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`include_internal`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.1Sep 16, 2026

TDQS

A4.8/5.0

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 goes beyond the schema by describing the response contents: each model carries a say_to_user line, target, and specs, and a server block with version and git SHA. It also warns that MCP servers are long-lived and may serve stale code, which is genuinely useful operational context.

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?

The description is front-loaded with the purpose, then gives usage guidance, parameter behavior, and an important operational note about stale MCP processes. Every sentence carries practical value and the length is appropriate for the tool's complexity.

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?

For a simple list tool with one optional parameter and an output schema, the description is complete. It tells the agent when to call it, what the response contains, how to present results, and how to handle the internal flag. Nothing needed for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides only a boolean include_internal with a default of false and no description. The tool description fully compensates by explaining exactly when to pass include_internal=true (only when the algorithm name, R², or log-target flag is needed for the agent's own reasoning) and what fields are withheld by default.

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: 'List all trained P2Predict models in the configured models directory.' This clearly differentiates it from sibling tools like get_model_info (which targets a single model) and predict (which uses a model). The purpose is unambiguous and immediately actionable.

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 explicitly says 'Call this first to discover which models are available,' giving a clear when-to-use directive. It also explains the condition for passing include_internal=true. However, it does not explicitly mention when to prefer sibling tools like get_model_info over this one, so the exclusion guidance is incomplete.

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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