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HyperRouteAI

hyperroute-mcp

Official
by HyperRouteAI

my_tool_report

Report your tool track record by capability region, showing outcomes and alternative options to inform better tool choices.

Instructions

The user's OWN track record on the tools they declared, per capability region: how their reported outcomes came out, and whether HyperRoute holds tested alternatives in the same region.

This is what turns a pinned tool into an informed choice. Deliver it when the user asks how their tools are doing, or when you notice a declared tool repeatedly underdelivering.

Two things to keep straight when you relay it: these are the USER'S OWN reports on their own tool, not HyperRoute measurements — nothing here tested their tool — and they are NOT on the same scale as a catalog tool's score. Say what their record shows and what tested alternatives exist; do not tell them their tool is worse. If they want HyperRoute to start preferring a better-scoring catalog tool in some region, that is update_my_tool(stance="benchmarked").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A4.9/5.0
Behavior5/5

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

The description transparently discloses that the data is user-reported, not measured by HyperRoute, and that the tool should not claim the user's tool is worse. It also mentions the related update action, providing clear behavioral expectations.

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 well-structured with the core purpose first, followed by usage context and important caveats. It is slightly verbose due to repeated emphasis on 'user's own reports', but remains clear and focused.

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?

The description fully covers what the tool does, when to use it, the nature of the data, and the follow-up action (update_my_tool). It provides sufficient context for an agent to decide when and how to invoke it correctly.

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 input schema has zero parameters, and the description does not need to explain any. The schema is fully covered, and there is no missing parameter information to add.

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 that the tool reports the user's own track record on declared tools, per capability region, including alternatives. It also distinguishes this from HyperRoute measurements, making its purpose unambiguous.

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?

The description explicitly states when to deliver the report: when the user asks how their tools are doing, or when a declared tool repeatedly underdelivers. It also provides actionable guidance on what to relay and what to avoid.

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