Get aggregate solver volume stats: the approximate total number of filled-intent records (fill documents, not distinct intents). Cached ~60s upstream.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Response format: 'json' for raw data or 'markdown' for formatted text | markdown |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses that the data is cached ~60s upstream and is approximate, which is critical for interpreting freshness and precision. This adds meaningful behavioral context beyond what annotations already convey.
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 compact sentences, front-loaded with the primary purpose. The second sentence adds essential nuance about the metric and caching without unnecessary filler. Every clause contributes value.
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 simple read-only getter with one optional parameter and no output schema, the description sufficiently specifies the return value ('approximate total number...') and key behavioral traits. No critical information is missing for correct invocation and interpretation.
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 100% for the single optional 'format' parameter, so the baseline is 3. The description does not add additional parameter semantics, but the schema fully explains the parameter's purpose and enum values.
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 states the tool's function: 'Get aggregate solver volume stats' with a specific metric definition ('approximate total number of filled-intent records'). It also distinguishes the metric from a potentially related sibling ('not distinct intents'), effectively disambiguating from other volume tools.
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?
The description provides clear context for when to use this tool: when aggregate solver volume stats (specifically fill documents) are needed. However, it does not explicitly name alternatives or exclusions, though the clarification about 'filled-intent records, not distinct intents' helps guide selection relative to sibling tools.
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.
Several tools have overlapping scopes: sodax_get_token_supply, sodax_get_total_supply, and sodax_get_circulating_supply all provide supply data, and the four money_market_* getters could be conflated. However, most tools are distinct and descriptions help.
The sodax_* tools follow a consistent get_<resource> pattern, but docs_* tools mix camelCase (getPage, searchDocumentation) with snake_case (list_tools), and there are non-get verbs like refresh and submit. This inconsistency makes the naming pattern less predictable.
With 40 tools, the server exceeds the 25-tool threshold for even a comprehensive API. Many endpoints are granular (e.g., separate supply tools) and could be consolidated, making the set feel bloated.
The tools cover a wide range of SODAX features: docs, money market, AMM, relay, solver, token data, and user positions. Missing are write operations (except relay_submit) and a few data endpoints like pool details, but for a query-oriented MCP it's largely complete.
