protocol_profileDeFi protocol analytics: TVL, yield pools, on-chain activity, health signals. Supports Raydium, Orca, marginfi, Drift, Jupiter, Kamino, Marinade, Jito, and more.
| Name | Required | Description | Default |
|---|---|---|---|
| protocol | Yes | Protocol slug (e.g. "raydium", "orca") or Solana program ID | |
| include_yields | No | Include yield pool data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It enumerates data categories but does not state whether the operation is read-only, describe return format, pagination, rate limits, or handling of unsupported protocols. This leaves significant behavioral ambiguity for an analytics tool.
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 exceptionally concise: two sentences that front-load the core purpose and data categories, followed by supported protocols. Every word contributes value, with no repetition or filler.
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 two-parameter tool without an output schema, the description provides a reasonable overview of the data scope. However, it lacks details on return shape, error cases, or any additional behavior, and with no annotations, the agent has limited context for invoking the tool in complex workflows.
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 documentation already covers both parameters thoroughly (protocol slug or Solana program ID, include_yields boolean with default). The description adds a broader list of supported protocol examples ('Raydium, Orca, marginfi...'), which is helpful but does not alter the baseline established by 100% schema description coverage.
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 as providing DeFi protocol analytics with specific data types (TVL, yield pools, on-chain activity, health signals) and a list of supported protocols. This makes the purpose clear and distinguishes it from sibling tools focused on tokens or wallets, though it lacks a direct action verb.
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 does not provide explicit guidance on when to use this tool versus alternatives. It lists supported protocols, which implies a target use case, but there is no mention of when not to use it or how it compares to sibling analytics tools like compare_tokens or token_trend.
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.
With 32 tools, several have overlapping purposes, such as wallet_history vs portfolio_history (both track wallet portfolio over time) and smart_money_flow vs smart_money_trenches (both follow smart money movements). However, most tools have clearly distinct scopes, and detailed descriptions help differentiate them.
All tool names follow snake_case with a predictable verb_noun or noun phrase pattern (e.g., enrich_token, compare_wallets, perps_market_trend). The consistent structure makes the set easy to navigate, even the 'perps_' prefix group is uniform.
32 tools is well above the 25-tool threshold, making the surface feel heavy. While the breadth reflects the wide domain of Solana analytics, the sheer number can overwhelm agents and increase the chance of selecting the wrong tool.
The tool set covers most aspects of Solana token/wallet/perp analysis, including enrichment, comparison, trend tracking, smart money flows, and perp market structure. Minor gaps exist, such as no direct historical OHLCV endpoint, but the existing tools handle core workflows well.