getWaterStreamflow
Queries live US river level, streamflow gauge height, and flow velocity metrics using the USGS National Water Information System.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
Queries live US river level, streamflow gauge height, and flow velocity metrics using the USGS National Water Information System.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
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 exist, so description must disclose behaviors. It only mentions 'live' data but omits read-only nature, rate limits, required permissions, or response format. Lacks critical transparency for a query tool.
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?
Single sentence with no excess. Efficient but could benefit from a brief second sentence on parameter format or return type without becoming verbose.
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?
Despite low complexity (1 param, no output schema), description fails to mention return structure or usage notes (e.g., data availability). Incomplete for an agent to invoke correctly without guessing.
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 0% for the single parameter. Description does not explain 'state' beyond name and example 'CA'. No format specification (e.g., two-letter code), allowed values, or constraints.
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 queries live US river level, streamflow gauge height, and flow velocity metrics using USGS NWIS. The verb 'queries' and specific resource make purpose unambiguous, and it distinguishes from siblings which cover other domains.
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 on when to use this tool vs alternatives (e.g., getFloodWarnings, getStreamTemperature). No prerequisites or usage context provided.
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 have overlapping functionality, e.g., auditNetworkHost combines DNS, SSL, and header checks that have dedicated tools (auditDnsSecurity, checkSslExpiry, auditSecurityHeaders). Multiple weather and blockchain tools also overlap in scope, making it difficult for an agent to choose the right tool.
Most tools follow a consistent verb_noun pattern (e.g., getAirQuality, checkDnsPropagation), but a few deviate (agentPreflight, capabilitiesDiff) and some use compound names (depositCoordinationBounty). Overall, the pattern is clear but not perfectly uniform.
With 56 tools, the server is far too large for a coherent MCP surface. The number suggests a collection of many unrelated APIs rather than a focused tool set. A typical well-scoped server has 3-15 tools.
While the tool set covers many domains, each domain has shallow coverage. For example, blockchain tools miss basic transaction sending and contract deployment; weather tools lack forecasts. The 'requestMissingData' endpoint acknowledges gaps, but the current surface is severely incomplete for a general-purpose API.