provetrade-mcp

Wake the ProveTrade analyzer

provetrade_warm_engine

Idempotent

Wake the sleeping Go analyzer by polling its /health endpoint and wait up to a time budget for it to become ready.

Instructions

Wake the Go analyzer by polling its public /health, and wait for it with a time budget.

IMPORTANT, KNOWN LIMITATION: server-side probes do not reliably wake this service. On production, 72 seconds of probing at 4-second intervals produced no entry in the analyzer's own log; the instance only started when a real browser request arrived, and then took about 38 seconds. So this tool may honestly return still_sleeping even though nothing is broken. When it does, the reliable fallback is to open https://provetrade.com/app in a browser — the page pings the analyzer from the visitor's side for exactly this reason.

Costs no LLM budget, but it does consume free-tier hosting hours. Not read-only: it starts a service. Call it when health reports sleeping, not speculatively.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`budget_ms`	No	How long to keep probing, in milliseconds. Default 90000, hard ceiling 180000. A cold boot measured on production took about 38 seconds.

Output Schema

TableJSON Schema

Name	Required	Description
`woke`	Yes	True only if the analyzer answered with a healthy JSON body.
`caveat`	Yes	The known limitation of programmatic warming.
`probes`	Yes	How many /health requests were sent.
`waited_ms`	Yes	Actual elapsed time, not the budget.
`last_status`	Yes	The last raw classification. 'down' here means unreachable or erroring rather than spun down, which waiting longer will not fix — worth distinguishing from still_sleeping.
`final_status`	Yes	The two outcomes the caller has to act on.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 10, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations by disclosing that the tool is not read-only, consumes free-tier hosting hours, may return still_sleeping even when healthy, and includes a documented production limitation with concrete timing evidence. This is exactly the behavioral context an agent needs.

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 core action, then uses a clearly labeled known-limitation section. The detail about the production probe is specific and instructive rather than filler. Every sentence contributes to correct invocation or expectation-setting.

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 tool with one optional parameter, an output schema, and annotations covering idempotence and non-read-only behavior, the description fills the critical gaps: when to call it, what it costs, how long it may take, and what to do if it fails. Nothing essential is missing.

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?

The single parameter budget_ms is already fully documented in the input schema with default, min, max, and context-setting example. The description adds little beyond restating 'time budget,' so the high schema coverage baseline of 3 is appropriate.

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 clearly states the tool's purpose: it wakes the Go analyzer by polling its public /health endpoint and waits with a time budget. This specific verb-resource pairing distinguishes it from sibling tools like provetrade_health, which reports health rather than changing it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: 'Call it when health reports sleeping, not speculatively.' It also explains when the tool may fail and gives a reliable fallback, giving an agent clear decision criteria and alternatives.

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

Deploy Server