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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 — 11 pre-mapped macro subjects ("fed", "btc", "eth", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. You do NOT have to use those exact keys: the topic is resolved through aliases and keywords, so "bitcoin", "fed rate decision", "inflation", "s&p 500" and "next pope" all land on the right subject, and resolution.topic_matched_by tells you whether it was an exact key, a known alias, a phrase found inside a longer question, or a single-keyword guess — treat "phrase" and "token" as a GUESS at what you meant. An unresolvable topic returns error:"mapping_failed" with mapping_stage:"topic_unrecognized" and known_topics[]; it never silently falls back to a default subject. (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. resolution is returned in BOTH modes and says how each side's identifier was picked (which Kalshi series was queried, how many events came back, whether the chosen one had quoted markets; which Polymarket search query ran and why that event won). 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. FEES: every top_spreads_pp and low_confidence_pairs[] row carries edge_pp_gross (== |spread_pp|), fees_pp, edge_pp_net, net_positive, and BOTH venues' taker fees itemised as kalshi_fee_pp and polymarket_fee_pp (plus polymarket_fee_rate, polymarket_fee_category, polymarket_fee_basis). Kalshi leg: fee = ceil(0.07 * contracts * P * (1-P) * 100) / 100 dollars per order, verified against kalshi.com/docs and corroborating explainers as of 2026-09-12. Polymarket leg: fee = shares × rate × p × (1-p) with rate by category (crypto 0.07, sports/economics/culture/weather/other 0.05, finance/politics/mentions/tech 0.04, geopolitics and world events fee-free), verified against Polymarket's own docs as of 2026-09-13 and read off each market's published fee parameters rather than inferred. Both amortized at a 100-contract reference size. Before fleet #1927 the Polymarket leg carried modeled gas only, which made every edge_pp_net here optimistic by up to ~1.75pp; spreads that no longer clear are the correction. Spread-crossing cost is still NOT modeled on the Polymarket leg (no live order book is fetched by this tool). spread.fees_note carries the same disclosure. RESOLUTION EQUIVALENCE (fleet #1909): every top_spreads_pp and low_confidence_pairs[] row now also carries resolution_equivalent ("true"|"false"|"unclear") and, when not "true", resolution_warning naming what differs — computed ONCE per event pair (not per leg) via resolution_audit/resolution_diff off one representative leg from each side, since the settlement mechanism is normally shared across every leg in one event. A non-equivalent or unclear pair is NEVER suppressed, only labelled — read resolution_warning before treating spread_pp as a real cross-venue disagreement rather than a difference in contract. spread.resolution_audit carries the full underlying audit (source/timestamp/timezone/precision/evidence_standard/void_handling for both sides) and spread.resolution_source_note is the standing disclosure explaining the methodology and its "unclear" caveat. Call resolution_audit/resolution_diff directly for a specific pair of legs if you need a non-representative-sample breakdown. 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
topicNoSubject to compare. Canonical keys: fed | btc | eth | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president — but aliases and keywords resolve too ("bitcoin", "fed rate decision", "ethereum", "inflation", "s&p 500", "us recession", "next pope", "2028 election"). Check resolution.topic_matched_by in the response: "exact"/"alias" is a curated pairing, "phrase"/"token" is a keyword guess.
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.

  1. Changed2 schema fields changed
    • changedInput schema / examples
      Previous value: -[
      -  {
      -    "topic": "fed"
      -  },
      -  {
      -    "topic": "btc"
      -  }
      -]New value: +[
      +  {
      +    "topic": "fed"
      +  },
      +  {
      +    "topic": "btc"
      +  },
      +  {
      +    "topic": "bitcoin"
      +  },
      +  {
      +    "topic": "fed rate decision"
      +  }
      +]
    • changedInput schema / properties / topic / description
      Previous value: -"Pre-mapped: fed | btc | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president"New value: +"Subject to compare. Canonical keys: fed | btc | eth | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president — but aliases and keywords resolve too (\"bitcoin\", \"fed rate decision\", \"ethereum\", \"inflation\", \"s&p 500\", \"us recession\", \"next pope\", \"2028 election\"). Check resolution.topic_matched_by in the response: \"exact\"/\"alias\" is a curated pairing, \"phrase\"/\"token\" is a keyword guess."
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

It adds rich behavioral detail beyond the readOnly/openWorld/idempotent hints: exact fee calculation methodology and versions, the limitation that spread-crossing cost is not modeled, clarification that temporal_alignment_unknown is not the sample aligns, and the rule that non-equivalent pairs are never suppressed. This directly changes how an agent would interpret the tool's answers.

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

Conciseness3/5

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

The description is well-organized with labeled sections, but it is extremely long, with multiple layers of fee detail, version numbers, and references to internal fleet batches. Every detail is informative, but token cost, and over-specification makes it harder for an agent to find the essential facts.

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?

With no output schema, the description carries the full burden of explaining the response structure, flags, error cases, fee semantics, resolution comparison, skip counters, and the meaning of aligment fields. It arguably over-explains, ensuring an agent can safely invoke and interpret the tool in non-trivial situations.

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?

Even though parameter schema covers the fields, the description adds crucial semantics: alias and keyword resolution for topic, the difference between exact/alias vs phrase/token verdicts, and how explicit kalshi_event_ticker/polymarket_event_slug override the mapped topic. These behavioral details are not discoverable from the schema alone.

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 first sentence explicitly names the tool's core behavior: cross-venue spread between Kalshi and Polymarket for the same resolving question. It then lays out two precisely defined modes and distinguishes itself by mentioning pre-mapped topics and explicit pairings.

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 clearly describes when to use topic mode vs explicit tickers, and explicitly warns that pre-mapped topics often are not tradeable and that 'phrase'/'token' matches are guesses. It recommends using resolution_audit/resolution_diff when a specific pair of legs is needed, but it does not name alternative sibling tools for similar arbitrage comparisons.

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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