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HyperRouteAI

hyperroute-mcp

Official
by HyperRouteAI

fetch_result

Retrieve large server-side results by reference, then page, search, or extract JSON paths to access the exact data you need when output overflows.

Instructions

Read a large result kept server-side (fetch_result('<ref>') in a reply). op: slice (offset, limit lines) | json_path (path=[key|index…]) | search (query). An overflow is never missing data: page until you have what you need.

Input Schema

NameRequiredDescriptionDefault
opNoslice
refYes
pathNo
limitNo
queryNo
offsetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose the key behavioral trait: overflow is not data loss, so the agent should page. It does not state whether the response signals remaining/truncated state, what happens on an expired ref, or any size limits, leaving meaningful behavioral gaps.

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?

Three compact sentences, front-loaded with purpose before the op reference and the pagination caveat. The op enumeration is telegraphic but dense rather than padded; nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter tool with zero schema descriptions and no output schema, the description covers how to call it but not what comes back — no indication of how paging progress is signaled or how to know you have everything. Adequate to invoke correctly, incomplete for confident iteration.

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?

Schema coverage is 0%, so this is strong compensation: `op` modes are enumerated and each is bound to its parameter (`offset, limit lines`, `path=[key|index…]`, `query`), and `ref` is explained by its origin in a reply. It still omits whether limit/offset are line-based for json_path and search, and any default/maximum values.

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?

States a specific verb and resource ('Read a large result kept server-side') and even shows the in-band handle format `fetch_result('<ref>')` that triggers it. No sibling tool in the list reads a server-side overflow buffer, so the agent can distinguish it immediately.

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 trigger condition is explicit — a reply containing a ref — and the three modes are mapped to their purposes (slice for lines, json_path for key/index traversal, search for query matching). It does not state any exclusions or failure cases (e.g., invalid/expired ref), so it falls 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.