Search FDA drug labels by brand name, generic name, or active ingredient — returns brand, generic, manufacturer, purpose, and indications. Source: openFDA (U.S. FDA), public data.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query (e.g. 'aspirin', 'metformin', 'insulin glargine') | |
| limit | No | Max results (1-20, default 5) |
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the source (openFDA, public data) and the returned fields, which gives useful context about data origin and output. It stops short of explaining error handling or limitations, but for a simple search tool this is adequate.
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: it starts with the action (search), specifies the resource (FDA drug labels), then lists return fields and source. Every phrase adds value, and there is no redundancy.
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 simple search tool with two parameters, the description provides sufficient context about what can be searched and what is returned. It does not explicitly describe the response structure (e.g., list vs. object), but since there is no output schema, the listed return fields mitigate that gap. The overall picture is clear enough for an agent to use the tool effectively.
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 schema description coverage is 100%, meaning both 'q' and 'limit' are already fully documented. The description adds no new parameter-specific details beyond what the schema provides, 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 searches FDA drug labels by brand name, generic name, or active ingredient, and lists specific return fields. This distinguishes it from sibling tools like fda_drug_labels and health_drug_lookup by focusing on label search with particular output fields.
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 implies usage when one needs to search drug label information by name, but it does not explicitly mention alternatives or exclusions. Similar FDA-related sibling tools exist, so clearer guidance on when to use this tool vs. others would improve the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tool groups have overlapping purposes: fda_drug_labels vs health_drug_search, fda_recalls vs health_recalls, fx_official_rates vs treasury_fx_rates, treasury_debt vs us_debt_current, and get_gdp vs get_bea_gdp. These near-duplicates create real ambiguity for an agent deciding which tool to call.
Names mix verb-led styles (get_, search_, compare_, screen_) with domain-led styles (fx_, treasury_, uk_, health_, eurostat_, datausa_). Within the same domain, similar actions use different patterns (get_gdp vs eurostat_gdp vs imf_indicator), making the set feel inconsistent and hard to predict.
75 tools is extreme for any MCP server, especially when many tools are redundant or cover unrelated domains (weather, earthquakes, scholarly search, air quality) outside the stated SEC/economics/demographics/FX focus. This overwhelms agents and burdens context windows.
Core domains like SEC financials, major economic indicators, basic demographics, and current FX rates are well covered. However, gaps remain: no historical FX rates, no stock price/quote tool, limited demographic breakdowns, and no ability to fetch full SEC filing text. Some operations end in dead ends.