latest_boosted_tokensTokens being actively promoted on DEX Screener.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| boosts | No | Latest boosted tokens | |
| schemaVersion | No | API schema version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not contradict the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint). However, it adds no additional behavioral insights beyond what annotations already provide, such as rate limits, result ordering, or update frequency.
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 sentence with no wasted words. It is front-loaded and efficiently communicates the tool's purpose.
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?
Given the tool has no parameters, an output schema, and annotations, the description is minimally adequate. However, it lacks context on what 'latest' means (e.g., time window) and how 'actively promoted' is defined, which could help differentiate from similar tools.
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?
The input schema has no parameters and is fully covered (100%). The description does not need to add parameter information, and the lack of parameters makes this dimension straightforward.
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 indicates the tool returns tokens being actively promoted on DEX Screener. However, the verb (list/fetch) is implied rather than explicit, and it does not distinguish from sibling tools like `token_boosts_top` or `latest_token_profiles`.
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?
No guidance is provided on when to use this tool versus its siblings. There is no mention of usage context, prerequisites, or alternatives, leaving the agent to infer when to invoke it.
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 clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six polymarket_* tools cover closely related prediction-market analysis. Long descriptions help differentiate them, but an agent could easily pick the wrong near-duplicate.
All names are snake_case and readable, but there is no consistent verb_noun pattern: some are verbs (search_pairs, validate_claim), some noun phrases (latest_token_profiles, entity_profile), and some prefixes (pipeworx_*, polymarket_*) cover only subsets. The naming is understandable but stylistically mixed.
37 tools is far too many for a server labeled Dexscreener, especially since the majority of tools have nothing to do with DEX data. Even if this is intended as an all-in-one data/research server, the count exceeds what the apparent scope justifies and many tools feel bolted on.
For the DEX Screener domain, the core surface is covered: pair lookup, token lookup, search, latest profiles, and boosts. The broader Pipeworx/prediction-market side also has strong coverage with memory, subscriptions, entity resolution, and research tools. Minor gaps exist — some tools feel redundant or exploratory — but there are no critical dead-end workflows.