LiquidVision — Crypto Derivatives Data

ask

Read-onlyIdempotent

ONE CALL instead of five: ask in plain words ('where is leverage stacked on BTC', 'why did ETH drop', 'is SOL funding crowded', 'biggest whales near liquidation') and get answer: short statements with the numbers in them, facts: the same numbers as JSON, sources: which endpoints were read and how old their data is, caveats. The question is read for its topics (liquidations, funding, open interest, whales, levels, sentiment; English or Russian) and only those endpoints are fetched. No model involved: facts, not opinion, so you do the judging. Free, no key. For the base rate of the current state call get_precedents; for a narrative with an LLM call explain_market.

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": {
      "question": "<question>"
    },
    "name": "ask"
  }
}

Input Schema

Name	Required	Description	Default
`coin`	No	Base asset, e.g. BTC, ETH, SOL (no USDT suffix).	BTC
`question`	Yes	The question in plain words, English or Russian.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 6, 2026
- addedInput schema / properties / coin / description
```
Added value: +"Base asset, e.g. BTC, ETH, SOL (no USDT suffix)."
```
- addedInput schema / properties / question / description
```
Added value: +"The question in plain words, English or Russian."
```
AddedNov 3, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, it discloses the internal routing behavior (topic extraction, only matching endpoints fetched), that no model is involved so output is deterministic facts rather than opinion, and that access is free with no key. These are non-obvious traits an agent could not infer from annotations alone.

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?

Front-loaded with the value proposition, then return fields, then routing rules, then the alternatives — each sentence carries distinct information. Roughly 90 words with no filler or repetition.

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?

With no output schema present, the description fully carries the burden by naming and explaining all four return keys and their data-freshness metadata. Combined with 100% schema coverage on inputs, an agent has everything needed to call and interpret it correctly.

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 earns extra credit by supplying example question phrasings that demonstrate the expected shape and scope of `question`, plus explicit bilingual support. The `coin` parameter is left to the schema, which is acceptable given the schema documents it fully.

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 opens with a specific framing — 'ONE CALL instead of five' — then states exactly what it does: parses a plain-language question for topics and fetches only those endpoints, returning `answer`, `facts`, `sources`, `caveats`. It explicitly separates itself from `get_precedents` and `explain_market`, so an agent can route between them without opening schemas.

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?

It gives four concrete example questions showing the intended granularity, states the language support (English or Russian), and names both alternatives with the conditions that select them: `get_precedents` for base rates, `explain_market` for an LLM narrative. When-to-use and when-to-use-something-else are both 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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