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Polymarket–Kalshi Spread

polymarket_kalshi_spread
Read-onlyIdempotent

Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) topic — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit kalshi_event_ticker + polymarket_event_slug for custom pairings — BOTH modes run the identical token-overlap matcher, so the same disclosures apply to both. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning is a sentence and compatibility_codes[] the machine-readable form; BOTH can be non-empty on returned pairs, so read them even when matched_pairs>0. Codes: event_subject_mismatch (the two event titles share no subject words — probably not the same question), temporal_mismatch (they resolve in different months), temporal_alignment_unknown (the resolution month could not be parsed on one or both sides — NOT the same as confirmed-aligned; check each event's close/strike date yourself), non_equivalent_bet_shapes, no_candidate_pairs, unclassified_legs_excluded, pairing_unverified (set in EITHER mode whenever pairs are returned: the legs were matched by keyword and word overlap, not a shared resolution source). Each entry in top_spreads_pp carries its own flags[] (temporal_mismatch, temporal_alignment_unknown, event_subject_mismatch, low_token_overlap). A leg whose metric_type or match_subtype is "unknown" is NEVER paired — those comparisons land in spread.skipped_unclassified and, when the wording lined up, in spread.low_confidence_pairs[] for inspection only. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period, in EITHER mode; null means it could not be computed (see temporal_alignment_unknown), not that the two sides align. spread.fees_note is a standing disclosure: Kalshi charges per-contract trading fees, Polymarket does not, and this tool does not model Kalshi's fee schedule — every spread_pp is gross, not a net tradeable edge. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoPre-mapped: fed | btc | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president
kalshi_event_tickerNoExplicit Kalshi event ticker, e.g. "KXFED-26OCT". Overrides the topic-mapped Kalshi side.
polymarket_event_slugNoExplicit Polymarket event slug, e.g. "fed-decision-in-june-825". Overrides the topic-mapped Polymarket side.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint:false, and the description is fully consistent—it never implies a write or destructive action. Beyond that, the description discloses substantial behavioral nuances: the tool does not model Kalshi's fee schedule and returns gross spreads only, that 'compatibility_warning' can be non-empty even when matches exist, that temporal_alignment null means 'could not be computed' not aligned, and that legs with 'unknown' types are never paired. These are critical caveats an agent needs to interpret results correctly, making the description highly transparent.

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 very long, but it is well-structured and front-loaded: it starts with the core purpose, then introduces modes, response, and safety fields with clear labels (TWO MODES, RESPONSE, SAFETY FIELDS). Every major constraint and caveat is covered. While some redundancy exists (e.g., the phrase 'BOTH modes run the identical token-overlap matcher' appears twice), the complexity of the tool justifies the length. It is not overly bloated, but it could be slightly trimmed without losing content, so 4 rather than 5.

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?

Since there is no output schema, the description must explain return values, and it does exhaustively. It lists the response structure (leg-by-leg prices, top_spreads_pp, compatibility fields, codes, temporal_alignment, fees_note, skipped counters) and explains the meaning of nulls and non-null warnings. It also covers edge cases like unclassified legs being skipped. For a complex tool with two modes and multiple warning codes, this description leaves no critical ambiguity about what an agent will receive and how to interpret it.

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

Parameters5/5

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

The schema provides descriptions for all three parameters (100% coverage), so the baseline is 3. However, the description adds substantial meaning beyond the schema: it explains that `kalshi_event_ticker` and `polymarket_event_slug` 'override the topic-mapped side,' and that the explicit mode is for 'custom pairings.' It also clarifies that both modes run the same matcher, so the parameter interplay is fully explained. This goes well beyond the raw schema descriptions, making parameter semantics rich.

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 opens with a precise statement of what the tool does: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It then details two distinct modes (topic and explicit ticker/slug) and clarifies that both modes run the same matcher. This clearly distinguishes it from siblings like polymarket_arbitrage which focus on intra-venue arbitrage, so an agent can quickly differentiate.

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

Usage Guidelines5/5

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

Usage guidance is explicit and actionable. It says 'TWO MODES' and explains when to use each: the topic mode for 10 pre-mapped shortcuts, and the explicit mode for custom pairings. It also warns that 'most pre-mapped topics return compatibility_warning today' and 'pre-mapped ≠ tradeable,' steering the agent to verify compatibility rather than assume results are valid. No alternatives are mentioned for this tool, but within its scope the guidance is complete.

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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