New Tokens
get_new_tokensMost recently launched pump.fun tokens.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
get_new_tokensMost recently launched pump.fun tokens.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that results are scoped to recently launched pump.fun tokens, but does not disclose return shape, sorting behavior, or any dynamic/real-time behavior. It neither contradicts nor substantially enriches the annotation-provided context.
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 one concise, front-loaded sentence with no filler. Every word contributes to meaning, and the key qualifier 'Most recently launched' appears first, making the definition immediately scannable.
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 very simple read-only listing tool with one optional, well-constrained parameter, the description plus annotations cover the core safety profile and selection intent. It does not document return fields, but the absence of an output schema and the straightforward nature of the tool make this a minor gap rather than a critical omission.
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%, so the description must compensate for the undocumented parameters. The description never mentions the 'limit' parameter; only the parameter name and constraints provide meaning. This is insufficient for a low-coverage schema.
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 states a specific verb and resource ('get new tokens') and defines the exact scope: 'Most recently launched pump.fun tokens.' This clearly differentiates it from siblings like get_trending_tokens, which are about trending activity rather than launch recency.
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 phrase 'Most recently launched' implies the use case of discovering newly-launched pump.fun tokens, but the description gives no explicit when-to-use or when-not-to-use guidance. It does not name alternatives such as get_trending_tokens, so the agent must infer the selection criteria from context.
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 have distinct purposes, but get_coin_intel and get_oracle_conviction overlap in providing intelligence signals; descriptions help differentiate but still may cause confusion.
Naming patterns are mixed: some use snake_case (get_bonding_curve), others use camelCase (sns_reverseLookup), and kol_leaderboard lacks a prefix that others like pumpfun_ have.
19 tools cover a broad range of pump.fun functionality, from token details to claims and social analysis; the count feels appropriate for the scope.
The tool set covers virtually all core aspects of pump.fun ecosystem: token info, intelligence, trading, claims, AMM quotes, metadata upload, SNS, and social sentiment, with no obvious gaps.