token_securityToken security heuristics: honeypot signals, proxy detection, LP data. Trading agents use this to avoid rugs. [price: $0.01/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | base (default) | |
| address | Yes | Token contract address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool analyzes honeypot signals, proxy detection, and LP data, and mentions a price per call. It does not describe side effects, output shape, or error/edge-case behavior, but the read-only heuristic nature is reasonably inferred.
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 concise and front-loaded: what the heuristics cover, the use case, and pricing. Every sentence contributes distinct information with no 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?
With no output schema and no annotations, the description only partially fills the gap. It names analysis categories but not the response format or how the signals are returned, so an agent may need to probe the output. The required parameter is in the schema, so invocation is still feasible.
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%, so the schema already documents the two parameters and their meanings. The description adds no extra parameter-level detail beyond the schema, which is acceptable but not enhancing.
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's domain and purpose: token security heuristics covering honeypot signals, proxy detection, and LP data, for avoiding rugs. It lacks an explicit verb like 'checks' or 'evaluates', but the intent is unmistakable and distinct from all siblings.
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?
States that trading agents use this to avoid rugs, which implies the intended context. However, it does not explicitly state when to prefer this over alternatives or provide any exclusions or prerequisites.
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 several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.