FDA drug label lookup (openFDA) — Official FDA label data for a drug by brand or generic name: indications, warnings, dosage. Source: openFDA. JSON. Price: $0.005 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | brand or generic drug name |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: authoritative source (openFDA), response format (JSON), and cost/payment mechanism ($0.005 USDC via x402). It does not disclose rate limits or error behavior, but the added context goes beyond what annotations provide.
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?
Every element earns its place: purpose, input scope, key data fields, source, format, and price are all compressed into one efficient sentence. No filler or repetition. The structure front-loads the core purpose before supplementary details.
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 single-parameter read-only lookup with no output schema, the description covers the essential context: what data comes back, what input is expected, source, format, and cost. It is missing details on fallback behavior for unknown drugs and exact response shape, but these are minor for such a simple 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?
Schema description coverage is 100% for the single parameter 'name' ('brand or generic drug name'). The description repeats this same semantic, reinforcing it but not adding new meaning. Baseline 3 is appropriate because the schema already carries the full parameter documentation.
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 ('lookup') and resource ('FDA drug label'), with clear scope: drug by brand or generic name, covering indications, warnings, and dosage. It distinguishes itself from siblings like fda_recalls by focusing on label content rather than recall events. The purpose is immediately unambiguous.
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 clearly implies when to use: when you need official FDA label data for a named drug. It does not explicitly name alternative tools for similar lookup needs (e.g., health_drug_brief, fda_recalls), but the scope 'by brand or generic name' and the mention of label-specific fields provide sufficient context. Lacks explicit exclusions or when-not-to-use 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.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.