FAERS adverse event reports for a drug by brand/generic name — report id, seriousness, reactions, outcomes. Source: openFDA (U.S. FDA) FAERS, public data.
| Name | Required | Description | Default |
|---|---|---|---|
| drug | Yes | Drug brand or generic name (e.g. 'Aspirin', 'atorvastatin') | |
| 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?
With no annotations, the description carries the burden. It adds context about the source (openFDA public data) and the nature of the data, but doesn't disclose rate limits, pagination, error behavior, or what happens when no reports are found. It's minimally transparent but not fully.
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?
One sentence, front-loaded with the core purpose, and includes the key returned fields and source. No wasted words.
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 query tool, the description is fairly complete: it tells what it returns, the lookup key, and the data source. The lack of an output schema is mitigated by listing the fields. It doesn't mention limit default or pagination, but these are handled by the schema.
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 both parameters (drug and limit) already have descriptions. The description adds the context that the drug is searched by brand/generic name, but doesn't provide additional meaning beyond the schema. This matches the baseline for high schema coverage.
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 provides FAERS adverse event reports for a drug by brand or generic name, listing specific fields (report id, seriousness, reactions, outcomes). It differentiates from sibling tools like health_adverse_events_by_ndc by emphasizing the name-based lookup.
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 this tool should be used when you have a drug name (brand or generic), but it doesn't explicitly exclude by-NDC lookups or name alternative tools. There is no 'when not to use' guidance, making the usage context clear but not comprehensive.
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 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.