openFDA drug label search. Req: { q?: string, limit?: int } (x402: 0.001 USDC per call)
HTTP equivalent: GET /x402/openfda-drug-label Payment: x402 protocol — send X-PAYMENT header with base64 payment payload, or X-API-Key / X-Owner-Key / Bearer token to bypass.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no behavioral annotations (e.g., readOnlyHint), the description carries the full burden. It discloses payment requirements and HTTP endpoint but does not describe data return behavior, error conditions, or side effects. The tool is presumably read-only, but this is not explicitly stated.
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 structured, with clear sections for requirements, HTTP equivalent, and payment details. Every sentence adds value, and the format is easy to parse.
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 output schema and no behavioral annotations, the description should explain what results look like or any limitations. It only covers payment and endpoint, leaving the agent guessing about the response format, error handling, and actual data structure.
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?
Despite the input schema being empty, the description explicitly lists the accepted parameters q (string) and limit (int), providing type information that would otherwise be missing. However, it does not explain the meaning or usage of these parameters in more detail.
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 as an 'openFDA drug label search', which specifies the resource (drug labels) and the action (search). This distinguishes it from sibling FDA tools like adverse events, recalls, and warning letters.
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 the many sibling FDA tools. The description only states the tool's function and requirements, leaving the agent to infer when it should be selected.
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.
Multiple tool pairs are exact duplicates (e.g., wallet_analysis/wallet_analyzer, honeypot_check/rugpull_detector, defi_yield/defi_yield_rates) with identical descriptions, and many overlapping scanners (squeeze_scanner variants). An agent cannot reliably distinguish these tools.
Names are all snake_case, but conventions vary wildly: some are resource names (btc_price), some are actions (list_magnets), some are generic utilities (calculator), and duplicates use different naming patterns for the same function (wallet_analysis vs wallet_analyzer). No consistent verb_noun or resource_action pattern.
139 tools is an extreme count for any MCP server, far exceeding the 50+ threshold. The scope is a grab-bag of unrelated domains (crypto, FDA, SEC, utilities, web scraping), suggesting no curation.
The set is a shallow pile of read-only queries and utilities with no lifecycle management for any one domain. Many sub-areas have only one or two tools, and duplicates occupy slots where distinct operations should be. The lack of a clear domain makes completeness impossible to assess, but the surface is redundant rather than comprehensive.