provetrade-mcp

ProveTrade gateway metrics

provetrade_metrics

Read-only

Monitor gateway error rates and audit latency. Classified counters pinpoint causes like parse failures or rate limits.

Instructions

Read the gateway's operational counters and latency percentiles from GET /metrics-lite.

Use it to answer "is the error rate up?" or "how slow are audits right now?" — the counters are classified (parse failures, analyzer down, rate limits, LLM degradation) so a spike points at a cause.

Requires METRICS_TOKEN in the environment. It is never a parameter and never appears in any output. Without it the tool returns TOKEN_MISSING and nothing else breaks.

Read-only, no cost. The numbers are per-process and reset on redeploy.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`counters`	Yes	Per-class request counts. Classes: ok, bad_request, invalid_file_type, upload_limit, rate_limit, parse, analyzer_down, server_busy, llm_unavailable, other. Every class is always present — a zero is information too.
`latency_ms`	Yes	Percentiles over a bounded recent window; the process resets them on restart.
`total_requests`	Yes	Sum of every request class except llm_unavailable, which is a side signal.
`uptime_seconds`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 10, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, but the description adds valuable context beyond that: the METRICS_TOKEN environment requirement, the TOKEN_MISSING error behavior, that it is read-only with no cost, and that numbers are per-process and reset on redeploy. No contradiction with annotations. This is exactly the kind of added behavioral context the rubric rewards.

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 slightly longer than necessary but each sentence adds value: purpose, usage, auth, and reset behavior. It is front-loaded with the core action and usage. A small trim could make it tighter, but it remains efficient and well-structured.

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 no-parameter, read-only metrics tool with an output schema, the description fully covers what an agent needs to decide when to call it, how to handle auth (token missing), and what the data represents (per-process, reset on redeploy). Nothing important is missing.

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?

The tool has zero parameters, so the baseline is 4. The description clarifies that METRICS_TOKEN is never a parameter and never appears in output, which is useful for setting agent expectations about invocation. There is nothing else to explain since the schema is empty and coverage is 100%.

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 states the specific action ('Read the gateway's operational counters and latency percentiles') and the source endpoint ('GET /metrics-lite'), with concrete use-case examples ('is the error rate up?'). It clearly distinguishes this from sibling health/deploy tools by focusing on operational counters and latency, so an agent can tell it apart without inspecting other schemas.

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?

It gives explicit context for when to use the tool (answering error-rate and latency questions) and even implies a diagnostic pattern (a spike points at a cause). However, it does not explicitly mention when NOT to use it or compare with siblings like provetrade_health or provetrade_deploy_status, so exclusions/alternatives are absent.

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

Deploy Server