VotePredictor Elections

Member of Congress

member_profile

Read-onlyIdempotent

A member of Congress by name or bioguide id: party, state, ideology score, reported net worth from financial disclosures, and the 2026 race for their seat.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`who`	Yes	Who to look up: a surname ('Pelosi'), a full name, or a bioguide id ('P000197'). If several sitting members match, the tool returns the candidates rather than choosing one.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	Yes
`name`	Yes
`found`	Yes
`party`	Yes
`state`	Yes
`chamber`	Yes
`summary`	Yes
`race_2026`	Yes	Race id for their seat, if it is on the 2026 ballot AND they still hold it
`bioguide_id`	Yes
`net_worth_year`	Yes
`net_worth_range`	Yes	[low, high] in dollars. Disclosures report brackets, never an exact figure.
`ambiguous_matches`	Yes	Populated when the name matches several sitting members; ask the user which, do not pick one
`currently_serving`	Yes	False for a former member. Lookup still resolves them, but they are not in office and are not a candidate.

TDQS

B3.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful context by listing what data is returned, but it does not disclose edge-case behavior such as returning all matching candidates for an ambiguous name; that nuance appears only in the input schema. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one compact sentence with no fluff, front-loading the resource and lookup key before listing the data points. It is slightly redundant with the title 'Member of Congress' but otherwise every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter, read-only lookup with an output schema and comprehensive annotations, the description provides sufficient orientation: it names the lookup key and the returned data points. The ambiguity fallback is already documented in the schema, so no critical information is missing for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the 'who' parameter thoroughly, explaining surname, full name, bioguide-id formats, and the multiple-match behavior. The description merely restates 'by name or bioguide id' and adds no additional semantic detail beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a member-of-Congress lookup keyed by name or bioguide id and enumerates the returned fields: party, state, ideology score, reported net worth, and the 2026 race. It is specific and effectively distinguishes itself from the forecast-oriented sibling tools, though it lacks a verb and is phrased as a noun phrase rather than an action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance about when to use this tool versus alternatives such as chamber_outlook, forecaster_accuracy, or forecast_race. The intended use is only implied by the title and the return-field list, so an agent must infer when this is the right tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation5/5

Each tool targets a clearly distinct resource: chamber-level control, an individual race, forecaster performance, and a member of Congress. The descriptions sharply differentiate aggregation level (chamber vs. single race) and subject (forecasters vs. legislators), so no two tools plausibly answer the same query.

Naming Consistency4/5

All four names are two-word snake_case compounds (chamber_outlook, forecaster_accuracy, forecast_race, member_profile), which is a consistent style. However, forecast_race inverts the entity_facet pattern used by the other three (race_forecast would match), creating a minor deviation.

Tool Count4/5

Four tools is slightly lean for an elections domain that could reasonably include race browsing or polling detail, but the scope is tightly focused and every tool earns its place. The count sits just under a fully-featured server while remaining well within a reasonable range.

Completeness4/5

Core workflows are covered: aggregate chamber outlook, per-race forecasts, forecaster credibility, and legislator context, with chamber_outlook even enabling race discovery via the 'moved most' list. Minor gaps exist—no race list/browse endpoint, no historical results, no poll-level detail—but agents can work around them without hitting dead ends.

Resources

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