crypto_price
Current USD price for one token symbol from live market data, with source attribution. $0.004/call via x402.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Single token symbol, e.g. ETH, BTC, SOL | BTC |
Oblique Markets
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Changed3 schema fields changed
- added
Input schema / properties / symbolAdded value: +{ + "default": "BTC", + "description": "Single token symbol, e.g. ETH, BTC, SOL", + "type": "string" +} - removed
Input schema / properties / symbolsRemoved value: -{ - "description": "Token symbols (BTC, ETH, etc.)", - "items": { - "type": "string" - }, - "type": "array" -} - removed
Input schema / requiredRemoved value: -[ - "symbols" -]
- First observed
TDQS
A3.6/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It adds useful context about live market data, source attribution, and cost, but does not disclose behavior such as error handling, response shape, or whether the price is cached/paginated.
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?
The description is a single sentence that efficiently packs purpose, data source, attribution, and cost. No wasted words or redundant details.
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 simple one-parameter price lookup, the description covers the essential purpose and adds cost and source attribution. The lack of an output schema is mostly mitigated by stating the price and source, though a brief mention of the response structure would make it 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?
The symbol parameter is fully documented in the schema with an example. The description only repeats 'one token symbol' and adds no additional semantics about format, case sensitivity, or accepted values.
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?
The description clearly states the tool provides the current USD price for a single token symbol using live market data. It mentions the specific resource (token price) and scope (one symbol), but does not explicitly contrast with similar siblings like token_metrics or market_intel.
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?
Usage is implied: use when you need a quick USD price for one token. However, there is no explicit guidance on when to choose this over sibling tools, nor any mention of limitations or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.