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routstr-mcp

routstr-mcp

by ashen0x
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Ask a Routstr model

ask

Send a prompt to any available Routstr model and receive its response, charged in sats from your prepaid balance with cost shown and a per-call cap.

Instructions

Ask any model sold on Routstr and get its answer, paid in sats from the user's prepaid balance. Good for a second opinion, a model with different strengths, or a cheap model for a simple job. The cost is shown.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>",
      "prompt": "<prompt>"
    },
    "name": "ask"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel id, e.g. from find_models
promptYesThe full question, with all context the model needs: it sees nothing else
systemNoOptional system prompt
max_satsNoMost this call may cost, in sats (default 100, at most 500).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations mark this as non-read-only and open-world, and the description adds genuinely useful behavior beyond them: calls are charged in sats from a prepaid balance and the cost is shown. It does not cover failure modes such as an insufficient balance or how cost is surfaced (before vs. after), which keeps it from a 5.

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 tight sentences, front-loaded with what the tool does and the pricing model, with zero filler. "The cost is shown" is economically stated but slightly ambiguous about when the figure appears, a minor blemish.

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 4-parameter call tool with full schema coverage, the description covers purpose, cost mechanics, and spend source, which is what an agent needs before invoking it. With no output schema, a brief note on the shape of the answer or on insufficient-balance behavior would have made it fully complete.

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?

Schema coverage is 100%, and the schema itself explains model id, the self-contained prompt, the optional system prompt, and the max_sats cap with its default and bound. The description adds nothing further about parameters, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Ask any model sold on Routstr and get its answer, paid in sats" states a specific verb (ask), resource (a model), and the payment mechanism, which is the defining trait of this tool. It does not name or distinguish itself from the sibling council (multi-model deliberation) or find_models, so an agent must infer the single-model nature from the singular "any model".

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 description supplies concrete use cases: "a second opinion, a model with different strengths, or a cheap model for a simple job," which gives an agent real selection context. It stops short of explicit exclusions or naming alternatives (e.g. use council for comparing several models), so no routing rule is spelled out.

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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