Deep scan any token: price, liquidity, volume, market cap, age, trading activity, DEX info. Search by name, symbol, or contract address.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Chain ID (e.g. 'solana', 'ethereum') | |
| query | No | Token name or symbol (e.g. 'PEPE', 'WIF') | |
| address | No | Token contract address |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesAdded value: +falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool's behavior by listing what it returns (price, liquidity, volume, etc.), which is helpful. However, it does not mention limitations (e.g., supported chains, performance implications, or behavior when no match is found), leaving gaps in transparency. It claims 'any token' without qualification.
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, front-loaded sentence that immediately conveys the tool's purpose ('Deep scan any token') and then expands with concrete details. No wasted words or redundant information; it is appropriately concise for the tool's breadth.
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 absence of an output schema and annotations, the description does a good job of indicating what data the tool returns by listing key metrics. It does not explain output structure or potential error cases, but for a straightforward scan tool with three optional parameters, the description is reasonably complete. It could be more thorough about chain support or data freshness, but it still meets the needs of an agent choosing the tool.
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 already provides 100% coverage with descriptions for each parameter (chain, query, address). The description adds that search is by 'name, symbol, or contract address,' but this largely mirrors the schema's query and address descriptions. It does not add new semantic meaning beyond what the schema already specifies, so the baseline of 3 applies.
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 performs a 'Deep scan' of 'any token' and lists specific data points (price, liquidity, volume, market cap, age, trading activity, DEX info). It uses a specific verb (scan) and resource (token), and the scope is evident. It distinguishes itself from siblings like rug_check (security) and trending_memes (social) by focusing on comprehensive token metrics.
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 gives clear context for when to use this tool: when you need a broad set of token metrics. It says 'any token' implying it's a general-purpose scanner. However, it does not explicitly name alternative tools or state exclusions (e.g., 'use rug_check for security analysis'), so it's clear but lacks explicit when-not guidance.
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 overlap in function. chain_radar, trending_memes, and new_launches all focus on discovering hot or new tokens, with fuzzy boundaries between them. Similarly, token_scan and rug_check both analyze a specific token, though rug_check emphasizes risk. The descriptions help but an agent could easily select the wrong tool for a given task.
All tool names use lowercase snake_case with two components (e.g., chain_radar, token_scan, whale_watch). The pattern is consistent and predictable, though the second component is sometimes a noun (radar, score, watch) rather than a verb, making it slightly less uniform than a pure verb_noun convention.
With 9 tools, the server is well-scoped for a memecoin analysis platform. Each tool serves a distinct aspect of the domain—discovery, scanning, scoring, risk assessment, and health monitoring—without unnecessary redundancy or excessive bloat.
The tool set covers the core memecoin analysis workflow: discovering new tokens, scanning token details, assessing risk, and evaluating momentum/virality. Minor gaps exist, such as lack of historical data or direct comparison tools, but these are not critical for the server's stated purpose.