Get U.S. Census demographic data by state or county. Population, median income, poverty rate, unemployment, home values, and education level.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | state (default) or county | state |
| state | No | 2-letter state code (CA, TX, NY). Omit for all states. |
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 full disclosure burden. It states 'Get' suggesting a read operation, but lacks explicit mention of read-only nature, authentication requirements, rate limits, or data freshness. The behavioral traits are minimal.
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 efficiently conveys the tool's purpose and the types of data returned. Every word is purposeful, and there is no extraneous information.
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 tool's simplicity (2 parameters, no output schema, no annotations), the description provides adequate but not thorough context. It covers purpose and data fields but lacks usage guidance and behavioral details, making it minimally acceptable.
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%, so baseline is 3. The description does not add additional meaning to the 'level' or 'state' parameters beyond what the schema provides; it focuses on the output fields instead.
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 verb 'Get', the resource 'U.S. Census demographic data', and the scope 'by state or county'. It lists specific data fields (population, median income, etc.), distinguishing it from sibling tools like get_jobs or get_inflation.
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 for demographic data but does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use conditions or sibling comparisons are given, leaving the agent to infer context from the tool name and fields.
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.
Each tool targets a distinct economic domain (e.g., demographics, inflation, jobs) with no overlap. The descriptions clearly differentiate the data sources and use cases.
All tools follow a consistent `get_<topic>` pattern using snake_case, making the naming predictable and easy to navigate.
With 7 tools, the server provides a focused yet comprehensive set of macroeconomic indicators without being overwhelming. Each tool serves a clear purpose.
Covers major macroeconomic areas (employment, inflation, GDP, demographics, energy, treasury) but lacks minor categories like trade or consumer confidence. Gaps are slight.