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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 is exceptionally transparent about internal behavior, including compatibility_warning and compatibility_codes semantics, flag meanings, unknown legs never being paired, skipped_cross_type/subtype counters, gross-vs-net fees, and the fact that temporal_alignment null means 'could not compute.' This goes far beyond the readOnlyHint and idempotentHint annotations, which already cover safety.

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 densely structured, with front-loaded purpose, then mode selection, response behavior, safety fields, flag codes, and caveats. It repeats some enum values from the schema, but this repetition serves quick comprehension. Every section earns its place, though it could be tightened slightly by reducing the inline code enumeration.

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?

Despite having no output schema, the description orients the agent to every important response facet: leg-by-leg prices, top_spreads_pp with per-entry flags, skipped_unclassified, low_confidence_pairs, temporal_alignment, fees_note, and counters. It also warns about the rarity of real tradeable cross-venue spreads, ensuring the agent sets correct expectations.

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 input schema already documents all three parameters at 100% coverage, so the baseline is 3. The description adds meaningful extra semantics by explaining that both modes run the identical token-overlap matcher, that explicit parameters override the topic-mapped side, and that safety fields apply equally in both modes—information not derivable from the schema alone.

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 precise resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' which clearly identifies the tool's domain and differentiates it immediately from single-venue tools like polymarket_edges or polymarket_arbitrage. It further distinguishes the tool by explaining that it handles non-equivalent bet shapes and applies the same matcher in both modes.

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 through TWO MODES: pre-mapped topic shortcuts versus explicit kalshi_event_ticker + polymarket_event_slug pairings. It also warns that pre-mapped topics are not necessarily tradeable, which advises against over-trusting results. However, it does not name alternative sibling tools or explicitly state when to prefer a different tool such as polymarket_arbitrage.

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
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and deep_research, validate_claim, discover_tools, and suggest_questions all overlap with the ask_pipeworx family. The prediction-market cluster also has six tools whose distinctions require careful reading, making mis-selection likely.

Naming Consistency3/5

Most names are lowercase snake_case and reasonably descriptive, but no consistent verb_noun pattern holds across the set. entity_profile, recent_alerts, and pipeworx_trending are noun phrases, while compare_entities, resolve_entity, and validate_imei are verbs, and the useful ask_pipeworx_* and polymarket_* prefixes are not applied server-wide.

Tool Count2/5

33 tools is too many for an agent to navigate efficiently, especially since the underlying data surface is already hidden behind ask_pipeworx and dozens more tools. The set spans data research, prediction markets, memory, subscriptions, IMEI validation, dependency scanning, and llms.txt generation, making it feel like a grab bag rather than a focused server.

Completeness3/5

The main query/verify/research/monitor workflow is covered well: ask, grounded, deep research, claim validation, entity profiles, comparisons, subscriptions, and memory all exist, so common paths have few dead ends. However, the set is not a single coherent domain, and there is no direct fetch/read-record tool or prediction-market execution tool, leaving some reasonable follow-up actions implicit.