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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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructiveHint. The description goes far beyond this, explaining matching logic, compatibility codes, skipped pair classifications, temporal alignment caveats, fee treatment, and why returned spreads are gross not net. This is exemplary behavioral disclosure for a complex analytical tool.

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 highly structured, using labeled sections, explicit code identifiers, and a logical flow from purpose to modes to outputs to caveats. Almost every sentence carries operational value. It is slightly redundant in a few places, such as repeating the meaning of temporal_alignment_unknown and reiterating that flags can be non-empty, but overall it is dense and purposeful.

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?

There is no output schema, so the description must carry the full burden of explaining response behavior. It does so thoroughly: leg-by-leg prices, top_spreads_pp, compatibility_warning, compatibility_codes[], skipped_unclassified, low_confidence_pairs[], temporal_alignment, fees_note, and skipped comparison counters are all explained. An agent calling this tool has enough context to interpret results correctly.

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. The description adds meaningful context beyond the schema: it explains how topic auto-fetches the matching event on each venue, how explicit parameters override the topic-mapped side, and that both modes run the identical token-overlap matcher. This is useful added semantics rather than mere repetition.

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 a specific function: computing cross-venue spreads between Kalshi and Polymarket for matching resolving questions. It explains two modes and the response shape. However, it does not explicitly differentiate itself from closely related siblings like polymarket_arbitrage or polymarket_edges, so it is clear but lacks sibling differentiation.

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 strong context on when to use each mode: topic shortcuts for pre-mapped macro events and explicit ticker/slug pairs for custom comparisons. It also warns that most pre-mapped topics currently return compatibility warnings and that pre-mapped does not mean tradeable. It does not explicitly compare against alternative sibling tools, but it does guide usage within this tool well.

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

The tool set includes many similar Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta, deep_research) that all serve overlapping purposes, causing confusion. The Twitch-specific tools are distinct but are buried among many unrelated tools.

Naming Consistency3/5

Most tools use snake_case (e.g., get_streams, ask_pipeworx), but there is variation in patterns: some are verb_ noun (get_streams), some are just verbs (remember), and some are adjective_noun (recent_changes). No strong pattern across the whole set.

Tool Count2/5

With 35 tools, the server is bloated for its stated purpose as a 'Twitch' server. Only about 4-5 tools are Twitch-related; the rest are from a data platform (Pipeworx) and generic utilities, making the count feel excessive and unfocused.

Completeness2/5

For a Twitch server, the tool surface is severely incomplete. It lacks essential Twitch features like clips, follows, chat, or channel management. The few Twitch tools present cover only basic stream and user lookup.