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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

A4.6/5.0
Behavior5/5

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

Annotations already mark this read-only and non-destructive, and the description goes far beyond them by disclosing pairing caveats: compatibility fields can be non-empty even when pairs are returned, unknown legs are never paired, temporal_alignment null means 'could not be computed', and all spreads are gross because Kalshi fees are not modeled. It also defines every compatibility code and explains skipped counters, so the agent understands exactly what the tool will and will not guarantee.

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 it is logically ordered from purpose to modes to response fields to safety caveats, and it uses labeled all-caps segments to improve scannability. It is dense rather than redundant, though a single block of prose could be better formatted with bullets or section breaks for easier agent parsing.

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 semantics, and it does so thoroughly: leg prices, top_spreads_pp, compatibility codes, per-pair flags, temporal_alignment, fees_note, skipped counters, and the rarity caveat are all covered. Nothing essential for safe invocation or interpretation appears to be missing.

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 three parameters at 100% coverage, so the baseline is 3; the description adds meaningful mode logic by defining topic as a set of 10 shortcuts and the pair of explicit identifiers as an override path, and by noting that both modes run identical matching. It could specify combination rules more precisely, such as whether mixing topic with one explicit override is valid, but it compensates well overall.

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 by identifying exactly what the tool produces—a cross-venue spread between Kalshi and Polymarket for the same resolving question—and consistently ties every output field back to that cross-venue comparison. It clearly names both venues and the scope, so an agent can distinguish it from related sibling tools without inspecting their schemas.

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 gives clear operational context: two explicit call modes (topic shortcut vs explicit kalshi_event_ticker + polymarket_event_slug) and states that both modes run the same matching logic. It also cautions that pre-mapped topics often return compatibility warnings and are not automatically tradeable. It does not explicitly name alternatives or provide formal when-not-to-use rules, but the mode guidance and caveats give strong direction.

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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical router variants, and entity_profile/compare_entities/recent_changes/ai_visibility_check all inspect companies from overlapping angles. The six Polymarket tools form a tightly-overlapping mini-domain that further crowds the surface.

Naming Consistency3/5

All names are snake_case, but verbs are inconsistently used: many tools are noun phrases (citation_count, entity_profile, polymarket_edges) while others start with verbs (ask_pipeworx, compare_entities, validate_claim). Some prefixes like ask_* and polymarket_* help, but the overall verb/noun pattern is not coherent.

Tool Count2/5

37 tools is well above the typical 3–15 tool scope, and several are redundant variants (three ask_pipeworx modes) or hyper-specific sub-tools (six Polymarket tools). The server name suggests a focused citation service, but only six tools actually address citations, leaving the set bloated with unrelated data query and memory utilities.

Completeness3/5

For a general data-query gateway, the surface is quite broad and covers lookup, profiling, comparisons, subscriptions, and memory. But as an OpenCitations server it lacks a way to discover papers by topic and the breadth of the other domains is unwieldy and unowned—so notable gaps exist in any plausible stated purpose.