Paid ($0.005/call): DEX spread anomaly events (1-min |spread| above trailing 30-day p99). Descriptive statistics only; not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| pair | No | ||
| limit | No | ||
| since | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses important behavioral traits: the tool is paid ($0.005/call) and provides only descriptive statistics with a 'not investment advice' disclaimer. It also defines the anomaly criterion. However, it does not mention output format, ordering, or other side effects. Without annotations, this is a moderate level of transparency.
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?
The description is a single, tightly worded sentence that conveys the tool's core function, cost, and a disclaimer. It is well-structured and front-loaded with the most important information, with no wasted words.
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?
The tool has no output schema, no annotations, and sparse parameter descriptors. The description explains what an anomaly is but omits critical details such as the output structure, how 'since' should be formatted, and any other behavioral expectations. This leaves the agent under-informed for a data query tool.
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 description coverage is 0%, and the description provides no information about the parameters 'pair', 'limit', or 'since'. The meaning of these parameters is left entirely to inference from their names. The description does not compensate for the lack of schema descriptions, making it difficult for an agent to construct valid calls.
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 clearly identifies the tool's purpose: it returns DEX spread anomaly events, defined as 1-minute |spread| values above a trailing 30-day p99 threshold. This is specific and distinguishes it from the sibling 'dex_spread_summary' (which likely provides aggregate summaries, not anomaly events).
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?
There is no explicit guidance on when to use this tool versus alternatives. The description does not reference sibling tools like 'dex_spread_summary' or state conditions for choosing this over other data sources. The usage context is only implied via the anomaly definition, not explicitly stated.
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.
Most tools are clearly distinct: catalog, terms, regulatory events, and sanctions check each have unique purposes. The two DEX spread tools are similar but differentiated by summary vs. anomaly, though they could cause minor confusion without careful reading.
Tool names mix conventions: 'catalog' and 'reg_events' are noun phrases, 'get_terms' uses a verb, and 'sanctions_check' is a noun phrase with a verb component. All use snake_case, but there is no consistent verb_noun pattern across the set.
Six tools is a well-scoped number for a data-provider server, covering catalog, terms, and several distinct data products without being bloated or thin.
The surface covers the core functionality: discovering data via catalog, understanding terms, accessing regulatory events, DEX summaries/anomalies, and doing sanctions checks. Minor gaps exist (e.g., no tool for raw DEX spread data or detailed event lookup), but the main workflows are supported.