get_politicianA member of Congress: committees, board seats and other positions from annual Financial Disclosures (with resolved tickers), recent stock trades, and top PAC contributors from FEC data. Look up by name or bioguide_id; ambiguous names return candidates.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Member name | |
| bioguide_id | No | Bioguide id, e.g. P000197 |
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?
The readOnlyHint annotation already signals a safe read operation, so the bar for additional behavioral disclosure is lower. The description adds useful behavior details: it resolves tickers in financial disclosures, includes recent stock trades, and returns candidate options for ambiguous names. However, it doesn't mention what happens if no match is found, rate limits, or whether the returned profile includes all listed sections at once.
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 two sentences, front-loaded with the primary purpose and key data categories, then the lookup mechanics and ambiguity behavior. Every clause earns its place, and there is no fluff or repetition of the schema or title.
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 has no output schema and only two parameters, the description covers the main data contents and lookup behavior well. It could mention what happens on no match or how to disambiguate (e.g., additional fields returned), but for a read-only profile lookup with a simple interface, the description is reasonably complete.
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% — both 'name' and 'bioguide_id' are described in the schema. The description adds the relationship between the two parameters: they are alternative lookup keys, and ambiguous names yield candidates. But the schema already covers the syntax and examples, so the description only adds marginal semantic value beyond the structured data.
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 states a specific purpose: retrieving a member of Congress profile with committees, board seats, stock trades, and PAC contributors. It clearly distinguishes the lookup methods (name or bioguide_id) and mentions that ambiguous names return candidates, which sets expectations beyond a simple profile fetch.
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 when to use this tool: when you need a member's committees, financial disclosures, recent trades, or top PAC contributors. It doesn't explicitly name alternatives or state when not to use it, but the specificity of the data fields and the sibling tool context (award_totals, search_calls, etc.) make the use case fairly clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.