get_tokenAll trading pairs for a token address on one chain.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | Yes | Chain id | |
| token_address | Yes | Token contract address |
| Name | Required | Description | Default |
|---|---|---|---|
| pairs | No | Array of trading pairs for the token | |
| schemaVersion | No | API schema version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no additional behavioral context such as pagination, rate limits, or data freshness. It simply restates the data returned.
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 waste. It is concise but could be slightly more informative without being verbose.
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 simple tool and the presence of an output schema, the description is adequate but lacks details like whether results are paginated or ordered. It meets the minimum threshold but leaves some gaps.
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 coverage is 100%, and parameters are well-described with 'Chain id' and 'Token contract address'. The description does not add further semantic meaning beyond the schema, so a baseline score of 3 is appropriate.
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 purpose: retrieving all trading pairs for a given token on a specific chain. The verb 'get' and resource 'trading pairs' are specific, and it distinguishes from sibling tools like 'get_pair' and 'search_pairs'.
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 on when to use this tool versus alternatives. Siblings like 'get_pair' and 'search_pairs' exist, but the description does not differentiate use cases or provide context for selection.
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.