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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

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses substantial behavioral nuance: spreads are gross not net because Kalshi fees are not modeled, pairing_unverified is always set, unknown metric_type/match_subtype legs are never paired, and temporal_alignment null means 'could not be computed' not 'aligned.' It also enumerates compatibility codes and their meanings, giving 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.

Conciseness4/5

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

The description is long, but the tool is genuinely complex and every block earns its place: modes, response shape, safety fields, fees, and skipped counters. It is front-loaded with the central purpose and uses clear labeled paragraphs ('TWO MODES', 'SAFETY FIELDS', 'fees_note'). It is denser than ideal, but appropriate for the complexity.

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 must fully explain return semantics, and it does: leg-by-leg prices, top_spreads_pp units and direction (Kalshi − Polymarket), compatibility_codes and their meanings, skipped_unclassified vs low_confidence_pairs, and counter fields. It even covers edge-case semantics of null temporal_alignment and gross-versus-net spreads, leaving no critical gap for an agent deciding whether to call and how to interpret 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?

Schema coverage is 100%, so the baseline is 3, but the description adds significant meaning about parameter interplay: explicit kalshi_event_ticker and polymarket_event_slug override the topic-mapped side, and both modes share the same matching logic. It also lists the ten valid topic values, reinforcing the schema's enum-like list even though no formal enum is declared.

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 opening sentence states an explicit verb+resource+scope: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly differentiates from Polymarket-only siblings like polymarket_edges and polymarket_arbitrage by naming both venues and the exact computation. The two-mode explanation further pins down what the tool does.

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 clearly explains when to use each mode: topic shortcuts for pre-mapped macros, and explicit ticker/slug for custom pairings, with the note that both run the identical matcher. It warns that 'pre-mapped ≠ tradeable' and that most topics return compatibility_warning today. It does not explicitly name alternative tools for competing tasks, but for its own domain the usage guidance is rich 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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping research/lookup purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and validate_claim both verify claims against sources, and six polymarket_* tools overlap on edge/arbitrage detection. The three realestateapi_* tools are distinct but sit awkwardly beside 31 unrelated tools.

Naming Consistency2/5

Naming conventions are mixed: snake_case prefixed tools (realestateapi_property_search), domain-prefixed tools (polymarket_edges), verb-noun tools (ask_pipeworx, compare_entities), noun phrases (entity_profile, recent_changes), and bare verbs (remember, forget, recall). There is no consistent verb_noun pattern across the set.

Tool Count2/5

34 tools is heavy, and the vast majority belong to the Pipeworx platform rather than the Realestateapi identity — only 3 of 34 tools are real-estate specific. The count is not well-scoped for the server's stated purpose.

Completeness2/5

For a real estate API the surface is severely thin: search, detail, and skip-trace only, with no market trends, tax history, rental estimates, or comparable-sales data. For the Pipeworx meta-domain the coverage is broader, but the server presents as Realestateapi, making the domain coverage a mismatch.