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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.

TDQS

A4.3/5.0
Behavior5/5

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

The description is exceptionally transparent, going far beyond the annotations: it discloses that spreads are gross and ignore Kalshi fees, that compatibility_codes[] can be non-empty even when matched_pairs>0, that temporal_alignment null means unknown rather than aligned, and that pairing is keyword-based rather than source-verified. This adds substantial behavioral context beyond the readOnlyHint and idempotentHint annotations.

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 clear sections and front-loaded purpose followed by modes, response, safety fields, and caveats. Some repetition exists, such as the 'same disclosures apply to both' phrasing and multiple in-EITHER-mode reminders, but the density is largely justified by the many conditional safety behaviors an agent must not miss.

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 return values, and it does so thoroughly: leg-by-leg prices, top_spreads_pp, compatibility warnings and codes, temporal alignment, fee disclosures, skipped_cross_type/subtype counters, and low_confidence_pairs are all covered. An agent has enough context to invoke the tool correctly and interpret ambiguous or warning-laden results.

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?

The schema already documents all parameters with 100% coverage, and the description adds meaningful mode semantics: topic maps to a pre-mapped macro shortcut while kalshi_event_ticker and polymarket_event_slug override the topic-mapped side. It also adds concrete ticker/slug examples and clarifies that both modes run the same matcher, enriching the schema without repeating it.

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 defines the resource as the cross-venue spread between Kalshi and Polymarket for the same resolving question, which distinguishes it from single-venue tools like polymarket_edges. However, it is phrased as a noun phrase rather than an explicit verb like 'computes' or 'returns', and the title is nearly synonymous, so it slightly misses top clarity.

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 explains two usage modes (topic shortcuts vs explicit ticker/slug pairing) and warns that most pre-mapped topics are not tradeable, which is practical usage guidance. It does not explicitly name sibling alternatives or state when not to use this tool rather than e.g. polymarket_arbitrage, so it falls short of full exclusion guidance.

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

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that specify when to use each. Even overlapping functions like ask_pipeworx varieties are well-differentiated by mode (casual vs grounded vs multi-source).

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (e.g., query_layer, resolve_entity), but a few deviate with single-word verbs (forget, remember, recall) or noun_noun (layer_info). The pattern is mostly consistent with minor exceptions.

Tool Count3/5

With 33 tools, the count is high and borders on heavy. However, the tools span multiple domains (GIS, financial data, prediction markets, memory, subscriptions), and each serves a unique role, so the count is justifiable but could be streamlined.

Completeness4/5

The tool set covers a broad range of data access and analysis tasks relevant to the inferred domain of a multi-purpose assistant. While the ArcGIS portion is limited, the overall surface is well-populated with few obvious gaps.