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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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds substantial runtime transparency: safety fields, compatibility codes and their meanings, handling of unknown legs, skipped comparison counters, temporal alignment semantics, and the gross-vs-net fee caveat. This is far beyond what annotations provide and contains no contradiction.

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 front-loaded with the core purpose and then organized into modes, response, safety fields, and caveats. It is long and somewhat repetitive around temporal-alignment explanations, but nearly every sentence carries substantive operational detail, so the length is mostly justified.

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 comprehensively: leg-by-leg prices, spread fields, flags, warning codes, skipped categories, and fee treatment. It also warns about false confidence and tells the agent to read compatibility fields even when matched pairs exist. Nothing critical is 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful context: it explains the topic mode's pre-mapped behavior, that explicit ticker/slug override the mapped side, that both modes run the same matcher, and it lists concrete examples. This goes beyond the schema descriptions.

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 clearly states that the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, and it distinguishes itself by naming both venues and the two operation modes. It lacks explicit differentiation from closely related siblings like polymarket_arbitrage, so it stops short of a perfect 5.

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 explicit when-to-use guidance by explaining the two modes, how to choose between topic shortcuts and explicit ticker/slug pairings, and when returned spreads are meaningful versus when compatibility warnings apply. It does not name sibling tools as alternatives, but the in-tool usage context is clear and actionable.

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

A3.8/5.0
Disambiguation3/5

Most tools have distinct scopes, and the long cross-referencing descriptions help a lot. However, ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx by the server's own description, and the polymarket_* family plus ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping boundaries that could mislead an agent.

Naming Consistency4/5

The vast majority follow a clear verb_noun snake_case pattern like get_odds, list_sports, resolve_entity, and subscribe. A few exceptions such as odds_api_quota, pipeworx_feedback, polymarket_arbitrage, and recall break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

37 tools is well past the 25+ threshold and feels bloated for a server named 'Odds Api'. Many tools are meta-platform utilities — memory, feedback, trending, dependency scanning, llms.txt generation — that have no obvious connection to an odds API and make the surface hard to navigate.

Completeness4/5

The odds domain is well covered: list_sports, get_events, get_odds, get_event_odds, get_scores, quota tracking, and subscriptions form a coherent read/monitor workflow. Minor gaps exist — no historical odds or a single-event detail endpoint — but agents can complete core odds research and monitoring tasks without dead ends.