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TypeScript

Hybrid

Get Legislative Calendar

get_legislative_calendar

Read-only

Retrieve upcoming House/Senate floor votes that could impact stock tickers, with predicted vote windows and verified affected companies.

Instructions

Forward-looking legislative catalyst calendar: upcoming House/Senate floor votes (bills and Senate cloture motions) filtered to items that can move tickers. Each item includes the predicted vote window (start/end/granularity/confidence/provenance), marketRelevance (low/medium/high), significance (1-5), affected sectors with direction + mechanism, verified affected tickers with evidence quotes, pass outlook, considerationProcedure (suspension-calendar bills pass ~98% of the time), a conflictBadge when the sponsor traded a verified affected ticker, and tweet/plain summaries. An EMPTY calendar is a normal state — it means nothing market-relevant is scheduled in the window, not an error. Defaults: from=today, to=+14 days, minRelevance=low. IMPORTANT: affectedTickers contains VERIFIED rows only — every ticker carries a verbatim evidenceQuote substring-verified against the actual bill text (no hallucinated tickers). sponsorTradeFacts are restatements of public STOCK Act disclosures with verbatim amount brackets and BOTH transactionDate AND disclosureDate — always cite both dates together (disclosures lag trades by up to 45 days), and never present a fact as evidence of wrongdoing. Vote windows are predictions: check window.provenance for trust level ('uc_explicit' is exact; 'rule_xxii_computed' is a medium-confidence estimate) and window.granularity for how precise the window is (exact time vs day vs week).

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	Latest vote-window date inclusive (YYYY-MM-DD, default: today + 14 days)
`from`	No	Earliest vote-window date inclusive (YYYY-MM-DD, default: today)
`limit`	No	Maximum results to return (default: 25, max: 100)
`offset`	No	Pagination offset (default: 0)
`minRelevance`	No	Minimum market relevance: 'low' (default), 'medium', 'high', or 'none' (explicit opt-in to the full audit trail incl. non-market items — rarely useful)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.1.1Jul 17, 2026
- removedInput schema / properties / to / $ref
```
Removed value: -"#/properties/from"
```
- addedInput schema / properties / to / pattern
```
Added value: +"^\\d{4}-\\d{2}-\\d{2}$"
```
- addedInput schema / properties / to / type
```
Added value: +"string"
```
First observedv0.1.0Jul 1, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true. The description adds significant behavioral context: empty calendar is normal, vote windows are predictions with provenance/granularity, affectedTickers are verified verbatim, and sponsorTradeFacts require citing both dates with no suggestion of wrongdoing.

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 long but well-structured with front-loaded core purpose and subsequent details. Every sentence provides useful information, though it could be slightly more concise without losing value.

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

Completeness5/5

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

Given the complexity (5 parameters, output schema exists), the description covers behavior, edge cases, warnings, and interpretation thoroughly. It is complete for an AI agent to use correctly.

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

Parameters4/5

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

Schema coverage is 100%, so baseline 3. The description adds value by stating defaults for from/to and minRelevance, and explains the 'none' option for minRelevance as 'explicit opt-in to the full audit trail'.

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

Purpose5/5

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

The description clearly states the tool returns a 'Forward-looking legislative catalyst calendar' of upcoming House/Senate floor votes filtered to market-relevant items. This specific verb-resource combination distinguishes it from sibling tools like get_economic_calendar or get_earnings_calendar.

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

Usage Guidelines4/5

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

The description explains defaults (from=today, to=+14 days, minRelevance=low) and how to interpret an empty result (normal state, not error). It does not explicitly state when to use this vs. alternatives like get_policy_events, but the context is clear enough for an AI agent.

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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