2026 US election forecasts with a public accuracy record, plus congressional voting records.
Who is favoured to control the Senate or House after 2026: control probability, expected seats with an 80% range, how far it has moved since January, and the races that have moved most.
| Name | Required | Description | Default |
|---|---|---|---|
| chamber | Yes | Which chamber of Congress: 'senate' (51 seats for control) or 'house' (218). |
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| as_of | Yes | |
| chamber | Yes | |
| summary | Yes | |
| since_date | Yes | |
| range_80pct | Yes | [p10, p90] seats in 80% of simulations |
| seats_total | Yes | |
| seats_needed | Yes | |
| p_dem_control | Yes | Probability Democrats control the chamber |
| biggest_movers | Yes | |
| expected_dem_seats | Yes | |
| change_points_since | Yes | Points moved since the first snapshot, positive toward Democrats |
| seats_with_forecast | Yes | |
| seats_without_forecast | Yes | Publishing nothing, because a candidate left the ballot |
| seats_from_fundamentals | Yes | Forecast without any poll, because none exists |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful context about what the outlook exposes (probability, seat range, movement, biggest movers), but it does not disclose data source, freshness, or any behavior beyond those outputs.
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?
One information-dense sentence with the main question front-loaded, followed by a compact colon-separated list of returned information. There is no filler or redundant restatement of the title or annotations.
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?
For a single-parameter, read-only tool with an output schema and rich annotations, the description is nearly complete: it states the election cycle, the two chambers, and the output aspects. The only soft gap is the ambiguity of 'since January'—it does not specify January of which year—but this is minor given the 2026 context and existing structured metadata.
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?
The input schema fully describes the chamber parameter with an enum and per-value thresholds (51/218). The description mentions Senate/House but adds no parameter syntax, constraints, or formatting details beyond what the schema already provides, so the baseline 3 applies.
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?
Although phrased as a question, the description names the exact decision (who is favoured to control each chamber after 2026) and the concrete outputs (control probability, expected seats with an 80% range, movement since January, most-moved races). This clearly distinguishes it from siblings like forecast_race and member_profile without needing to open the schema.
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 domain is obvious—chamber-level control outlook for the 2026 elections—so an agent can infer when this tool applies. However, it does not explicitly state when to use this tool instead of forecast_race or forecaster_accuracy, and it gives no exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How accurate each election forecaster, model, prediction market and pollster has been on resolved races, adjusted for how hard each race was. Use this to answer 'whose election forecast should I trust'. Difficulty adjustment matters: on raw scores whoever rated the safest races wins.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many ranked entries to return, best first. Default 10. | |
| office | No | Restrict the board to one office, or ALL for every resolved race. | ALL |
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| metric | Yes | |
| office | Yes | |
| entries | Yes | |
| summary | Yes | |
| lower_is_better | Yes | |
| adjusted_for_difficulty | Yes | True. Raw scores reward whoever rated the safest races, so unadjusted rankings mislead. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that: results are about resolved races, adjusted for race difficulty, and raw scores would mislead. This is meaningful context, even though return-shape details are left to the output schema.
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?
Three short sentences with each one serving a distinct purpose: describe the ranking, state the use case, and warn about the difficulty adjustment. There is no filler or redundant restatement of the tool name.
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?
With only two optional parameters, complete schema documentation, and annotations covering behavioral guarantees, the description gives an agent everything needed to select and invoke the tool correctly. The trust-framing and difficulty caveat strengthen contextual completeness.
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%, and both 'limit' and 'office' already have clear descriptions in the input schema. The tool description does not add parameter-specific meaning beyond that, so the baseline of 3 applies.
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 identifies the resource ('election forecaster, model, prediction market and pollster') and scope ('resolved races'), and ties it to an applied question ('whose election forecast should I trust'). It is readily distinguishable from sibling tools like forecast_race, which focuses on individual race forecasts.
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 gives an explicit use case: 'Use this to answer whose election forecast should I trust.' It also provides a practical caveat about difficulty adjustment. However, it does not name sibling alternatives or state explicit when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Current VotePredictor forecast for one 2026 US election: win probability, predicted margin, whether it comes from polls or fundamentals, how far it has moved this cycle, and the candidates. Accepts a state name or code ('Ohio', 'OH') or a district ('CA-12'). Returns an explicit refusal for races where a candidate has left the ballot and no forecast is published.
| Name | Required | Description | Default |
|---|---|---|---|
| where | Yes | Where the race is: a state name ('Ohio'), a two-letter state code ('OH'), or a House district ('CA-12'). Pass what the user said. | |
| office | Yes | Which office: SEN for U.S. Senate, GOV for governor, HOUSE for a U.S. House district. |
| Name | Required | Description |
|---|---|---|
| url | Yes | Cite this |
| office | Yes | |
| rating | Yes | Safe D / Likely D / Lean D / Toss-up / Lean R / Likely R / Safe R |
| source | Yes | 'polls' where a poll-driven forecast exists, otherwise 'fundamentals' (lean, incumbency, national environment) |
| n_polls | Yes | |
| race_id | Yes | Canonical id, {year}_{OFFICE}_{location} |
| summary | Yes | One-paragraph answer suitable for reading aloud |
| location | Yes | State code, or district id for the House |
| p_dem_win | Yes | Democratic win probability, 0.01-0.99. Never 0 or 1: an election that has not happened is not certain. |
| state_name | Yes | |
| pred_margin | Yes | Predicted Dem-minus-Rep margin in points |
| dem_candidate | Yes | |
| rep_candidate | Yes | |
| movement_points | Yes | Change in win probability in POINTS since the first snapshot this cycle, positive toward Democrats |
| forecast_published | Yes | False when this site declines to publish a probability for the race |
| no_forecast_reason | Yes | Why nothing is published, e.g. a candidate has left the ballot. Do not substitute your own estimate. |
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 goes beyond annotations by disclosing the refusal behavior for races with no published forecast and specifying what forecast components the result includes, which adds meaningful behavioral context.
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?
Two sentences carry all the essential information with no redundancy. The return contents are front-loaded, the input formats and refusal edge case are in the second sentence, and every clause earns its place.
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?
With a fully documented schema, an output schema, and annotations covering read-only and idempotent behavior, the description fills the remaining gaps: scope, accepted input formats, and the refusal case. Nothing needed to select and invoke this tool correctly is missing.
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%, so the baseline is 3, but the description adds genuinely useful guidance: 'Pass what the user said' instructs the agent not to normalize input, and it clarifies valid forms ('Ohio', 'OH', 'CA-12'). The office parameter is already fully documented by the schema enum and descriptions.
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 verb ('forecast') and resource ('one 2026 US election'), and enumerates what the call returns: win probability, predicted margin, poll/fundamental source, movement, and candidates. This clearly differentiates it from the sibling tools, which concern chamber-level outlook, accuracy, and member profiles.
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 makes the intended use clear: a single race forecast for a state, state code, or district, with an office enum. It gives concrete input formats and notes an explicit refusal case, but it does not explicitly state when to prefer this tool over siblings such as chamber_outlook, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| 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. |
| 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. |
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.
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.
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.
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.
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.
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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U.S. federal policy data — bills, Congress members, voting records, and civic info.
Origin-signed US political data: 25,546 officials, bills, votes, positions with sources.
Nonpartisan U.S. Congress data: bill decodes, rep lookup, bilingual EN/ES, read-only, no accounts.
Prediction market data and crowd-sourced probability forecasts
Add one secure layer between your agents and this server.
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.
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.
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.
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.
