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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover read-only/idempotent safety, while the description adds substantial behavioral detail: compatibility_warning and compatibility_codes semantics, gross vs. net fee disclosure, the fact that unknown legs are never paired, and the standing warning that matched_pairs>0 does not mean confirmed-equivalent questions. It also clarifies that temporal_alignment null means 'could not compute' rather than 'aligned'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but tightly organized with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS, Codes) and front-loads the core purpose before explaining safety fields. Given the tool's complexity, no output schema, and need to communicate disclaimers, every sentence earns its place.

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 fully documents the response shape: per-venue leg prices, top_spreads_pp delta, per-entry flags, skipped_unclassified and low_confidence_pairs, temporal_alignment object, and fee note. It also explains edge cases like unparseable resolution dates and pre-mapped limits, leaving nothing essential for correct invocation or interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema already documents the three parameters at 100% coverage, the description enriches them with mode semantics: topic's pre-mapped shortcut list, how explicit ticker/slug parameters override the mapped side, and the guarantee that both modes run the identical token-overlap matcher. This explains interactions between parameters that the schema cannot convey.

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 defining the tool as a cross-venue spread between Kalshi and Polymarket for the same resolving question, with a clear contrast to single-venue pricing differences. It distinguishes itself from siblings like polymarket_arbitrage by focusing on cross-venue comparisons and explicitly describing the two modes and response contents.

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 explicit when-to-use context: pre-mapped topic shortcuts versus explicit ticker/slug pairings, and warns that pre-mapped topics often return compatibility warnings and are not necessarily tradeable. It does not, however, name any sibling tools as alternatives, so the when-not-to-use guidance is about the tool's own limits rather than about choosing another tool.

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

A3.6/5.0
Disambiguation2/5

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.