spreadKraken crypto exchange recent bid/ask spread history for a pair. Returns timestamped best-bid + best-ask. Use for liquidity studies.
| Name | Required | Description | Default |
|---|---|---|---|
| pair | Yes | ||
| since | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "pair": "XBTUSDT"
+ },
+ {
+ "pair": "ETHUSDT",
+ "since": 1234567890
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": {
+ "oneOf": [
+ {
+ "description": "Spread data",
+ "items": {
+ "description": "Time, bid, ask",
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ },
+ "type": "array"
+ },
+ {
+ "description": "Last spread time",
+ "type": "number"
+ }
+ ]
+ },
+ "description": "Recent spread indexed by pair",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation safe and read-only. The description adds behavioral detail about the timestamped best-bid/best-ask output, which is useful, but it does not address ordering, limits, or the 'since' parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler, front-loaded with the core purpose and use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only tool with an output schema and two simple parameters, the description covers the essential purpose and use case. It could be more explicit about the 'since' parameter but is otherwise sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema properties have no descriptions (0% coverage). The description clarifies 'pair' by context but leaves 'since' entirely unaddressed, requiring inference by the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('recent bid/ask spread history'), resource ('Kraken crypto exchange pair'), and return value ('timestamped best-bid + best-ask'), clearly distinguishing it from sibling tools like 'depth' and 'ticker'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides a clear usage context ('Use for liquidity studies'), which gives the agent a sense of when to apply this tool. However, it does not explicitly contrast with alternatives like 'depth' or 'ticker'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, such as the four ask_pipeworx variants and several Polymarket analysis tools. While descriptions are detailed, the distinctions are nuanced, and an agent may struggle to select the correct tool without careful reading.
Tool names mix styles: Kraken tools are short nouns (ticker, depth), while Pipeworx tools use various patterns (verb_noun, noun_noun). No single convention dominates, but names are generally readable and descriptive.
40 tools is high, combining two distinct domains. Many tools serve overlapping purposes, making the set feel bloated. A more focused server or consolidation of similar tools would improve scope.
The Pipeworx data tools cover a broad range of retrieval and analysis, including prediction markets, memory, and subscriptions. However, the Kraken tools lack trading functionality, and there are redundant tools that could be merged.