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Find Routstr models

find_models
Read-only

Search approved Routstr node models by keyword and sats price to compare AI model options and select a matching model.

Instructions

Search the models sold by approved Routstr nodes, cheapest first, with prices in sats. Every word of the query must match, e.g. "claude", "gpt 5", "qwen coder". Empty lists the cheapest.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoRows to return (default 20)
queryNoWords to match in the model id or name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Beyond readOnlyHint/openWorldHint it discloses the scoping to approved nodes, the deterministic cheapest-first ordering, the sat denomination, and the all-words-must-match filter rule. It stops short of saying what a result row contains or whether prices are live-quoted, so it is strong but not complete.

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?

Two tight sentences: purpose and constraints first, then the empty-query behavior, with three illustrative query strings that cost little and remove ambiguity. Nothing is redundant with the title or schema.

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

Completeness4/5

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

For a two-optional-parameter read tool with no output schema, the description covers purpose, filtering semantics, ordering, units, and the empty-input case. It leaves the shape of returned rows (model id, price, node) unspecified, which is the main residual gap.

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 100%, so the baseline is 3, but the description adds real semantics the schema lacks: the query is conjunctive ("Every word of the query must match") and an empty query is meaningful rather than absent. The limit parameter is only loosely implied by "Rows to return" in the schema.

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 ("Search the models sold by approved Routstr nodes") plus ordering and units ("cheapest first, with prices in sats"), which no sibling (ask, council, balance) covers. An agent can immediately tell this is a model-discovery tool rather than an inference or account tool.

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

Usage Guidelines3/5

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

The description gives one concrete usage rule — an empty query lists the cheapest — which is genuine when-to-use guidance for the no-argument case. It does not say when to call this versus ask/council (e.g. discover-then-invoke), and offers no exclusions, so guidance is implied rather than explicit.

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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