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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?

The description goes well beyond the readOnlyHint/idempotentHint annotations. It discloses that unknown metric_type or match_subtype legs are never paired, that compatibility_codes can be non-empty even when matched_pairs > 0, that temporal_alignment null means 'could not be computed' rather than 'aligned', and that Kalshi fees are not modeled. These are behavioral traits an agent needs to interpret results safely.

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 densely packed with field-level safety semantics and mode guidance that the agent cannot infer elsewhere, especially with no output schema. It is front-loaded with the core purpose, then modes, then response and safety fields. Some redundancy exists, but each paragraph earns its place given the tool's complexity.

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 that there is no output schema, the description does an exceptional job of documenting return behavior: leg-by-leg prices, top_spreads_pp, compatibility fields, per-entry flags, skipped counters, temporal_alignment semantics, and the fees caveat. It also covers the open-world reality that most pre-mapped topics currently return compatibility warnings, which is essential calibration for the agent.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful mode semantics: topic is a pre-mapped shortcut, explicit ticker/slug parameters override the topic-mapped side, and both modes run the same token-overlap matcher. This clarifies the relationship and exclusivity behavior beyond what the schema properties alone provide, though the schema already documents the individual values.

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 specific, resource-bound statement: "Cross-venue spread between Kalshi and Polymarket for the same resolving question." It clearly distinguishes the tool's scope from siblings like polymarket_arbitrage by focusing on matched spreads for the same question, and it names the two operating modes. This is more than a tautology and gives an agent an immediate mental model.

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 provides clear context for when to use the tool: comparing equivalent Kalshi and Polymarket outcomes on the same resolving question. It also gives operational guidance on modes, warns that pre-mapped topics often return compatibility warnings, and explains that spreads are gross rather than net tradeable. It does not explicitly name alternatives among siblings, but the context is strong enough to route an agent correctly.

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
Disambiguation3/5

Most tools have distinct purposes, but the ask/research family is crowded: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog and require careful description reading to select correctly. Prediction-market tools also overlap in scope, though each has a reasonably distinct angle.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb_noun (query_layer, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective_noun or brand-prefixed phrases. No consistent verb/noun ordering pattern exists across the set.

Tool Count2/5

34 tools is too many for the apparent focus, especially since the server is named 'Arcgis Phoenix' but only 3 of the tools are actually GIS tools. The bulk is a sprawling Pipeworx data/prediction-market ecosystem plus unrelated utilities, making the set feel over-stuffed and unfocused.

Completeness3/5

The Pipeworx data side is fairly complete for lookups, entity profiles, comparisons, validation, and subscriptions, but the ArcGIS Phoenix portion is only search/schema/query and lacks any analysis, geocoding, or editing capability. The presence of generate_llms_txt and scan_dependency highlights that the overall domain is undefined and therefore hard to call complete.