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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

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses extensive behavioral detail: compatibility warning codes, temporal alignment semantics, fee treatment, unclassified leg exclusion, paired-leg flagging, and skipped cross-type/subtype counters. It thoroughly explains edge cases like temporal_alignment_unknown being distinct from confirmed alignment, adding far more transparency than the annotations alone.

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 it is densely packed with necessary information and clearly structured with labeled sections: TWO MODES, RESPONSE, SAFETY FIELDS, codes enumeration, and fee disclosure. Every sentence contributes to correct usage or interpretation of results, and the most important caveats are front-loaded near the top.

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 thoroughly: response shape, top_spreads_pp, compatibility codes, temporal_alignment, fees_note, skipped counters, and low-confidence pairs. For a tool with this complexity and zero required parameters, an agent has enough context to invoke it correctly and interpret ambiguous or risky results.

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 schema already describes all three parameters with examples and override semantics (100% coverage), so the baseline is 3. The description adds meaningful context beyond the schema by explaining that explicit ticker/slug parameters override the topic-mapped side, that both modes run an identical token-overlap matcher, and how the topic list behaves as pre-mapped shortcuts. This compensates well without being redundant.

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 defines the resource: a cross-venue spread between Kalshi and Polymarket for the same resolving question, and explains the core idea of equivalent bet shapes. It does not explicitly differentiate this tool from siblings like polymarket_arbitrage or polymarket_edges, but the 'cross-venue' framing and detailed mode descriptions make the purpose unmistakable.

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 clear guidance on when to use each of the two modes: pre-mapped topic shortcuts versus explicit custom ticker/slug pairings, including that both modes run the same matcher. It does not explicitly name alternatives among sibling tools or state 'use X instead of Y', but it provides strong contextual usage guidance and warns that pre-mapped topics are not automatically tradeable.

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

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical when no routing candidate is active), ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions through the same underlying router. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) has substantial purpose overlap that requires reading long descriptions to disambiguate.

Naming Consistency3/5

Mostly snake_case, and the pipeworx_/polymarket_/ask_ prefixes give some structure, but conventions are mixed: some tools are verb_noun (list_municipalities, get_data), some are bare verbs (forget, recall, remember), and some are noun phrases (entity_profile, deep_research, bet_research, recent_changes). The inconsistent prefixing across meta-tools (ask_, deep_, entity_, scan_, validate_) makes the surface feel less predictable than it could be.

Tool Count2/5

35 tools is on the heavy side, but the bigger problem is that the server name 'Kolada Se' matches only 4 tools (search_kpi, list_municipalities, list_org_units, get_data), while the other 31 tools belong to unrelated domains (Pipeworx data routing, prediction markets, memory, subscriptions, AI visibility). This is a severe scope mismatch that makes the count feel bloated and unfocused.

Completeness3/5

For the Kolada domain named by the server, the surface is minimal: you can search KPIs, list municipalities, list org units, and fetch single-KPI data, but there is no multi-year bulk fetch, cross-municipality comparison, or unit-level data retrieval. The broader Pipeworx/prediction-market surface is fairly feature-complete, but it is not what the server name implies, so the set as a whole leaves the apparent domain thinly covered.