Get official NWS weather forecast (7-day periods: temperature, wind, short/detailed forecast) for a latitude/longitude. Source: NOAA National Weather Service (free for any purpose).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude (e.g. 39.7456) | |
| lon | Yes | Longitude (e.g. -97.0892) |
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 provided, the description carries the full burden. It adds useful context: it is official NWS data, free for any purpose, and returns 7-day periods with specific elements. However, it omits important behavioral details such as US-only coverage, output format (JSON), coordinate bounds, and potential error cases. This is a moderate disclosure.
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 that captures the core purpose and key output details, followed by a brief source note. No wasted words; all information is relevant.
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 low complexity (2 params) and absence of an output schema, the description provides a good overview of what is returned (periods, temperature, wind, short/detailed forecast) and the source. However, it misses the US-only limitation and does not describe the exact response structure, leaving minor gaps for an agent.
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% (lat and lon are fully described), so the description adds little beyond what the schema already shows. It merely refers to latitude/longitude as the input, which is already in the property descriptions. The baseline of 3 is appropriate.
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 uses a specific verb ('Get') and a clear resource ('official NWS weather forecast'), and explicitly states the output content (7-day periods, temperature, wind, short/detailed forecast) and input (latitude/longitude). It is distinct from sibling tools like nws_alerts and other data-heavy tools.
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 use when you need a weather forecast for a given latitude/longitude. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough to differentiate from nws_alerts (which is for alerts) and other financial/demographic tools.
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.