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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?

Annotations already carry readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description is fully consistent with them (analysis/read operation, no contradiction). Beyond that, it discloses substantial non-obvious behavior: the gross-vs-net fee caveat (Kalshi fees not modeled), the nuanced temporal_alignment null semantics ('null means it could not be computed... not that the two sides align'), the rule that 'unknown' metric_type/match_subtype legs are never paired, and the pairing_unverified caveat that matched pairs are keyword-based, not source-verified. This is exactly the kind of context annotations cannot express.

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 justified by the tool's genuine complexity (2 modes, 7 compatibility codes, alignment semantics, fee disclosure, no output schema to lean on). It is front-loaded with purpose and uses scannable delimiters (TWO MODES, RESPONSE, SAFETY FIELDS, Codes:) that aid navigation. There is some redundancy — temporal_alignment_unknown is explained in the code list, again in the temporal_alignment paragraph, and appears in the top_spreads_pp flags — and the codes are re-enumerated in multiple places, which could be tightened.

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?

For a tool with 3 parameters, 2 modes, and no output schema, the description carries the full burden of explaining return structure — and it does: leg-by-leg prices, matched spread[].top_spreads_pp, compatibility_warning/codes[], temporal_alignment month fields and null semantics, fees_note, skipped_cross_type/subtype counters, and low_confidence_pairs. Edge cases (unparseable dates, subject mismatches, inequality mismatches) are all covered. Nothing an agent needs to call and interpret this tool is missing.

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% — all three parameters have descriptions in the schema, so the baseline is 3. The description adds genuine value on top: it enumerates all 10 valid topic values, explains the mode interplay (topic auto-fetches both sides, while kalshi_event_ticker and polymarket_event_slug 'override' the mapped side, enabling mixed custom pairings), and clarifies that all three are optional yet mode-conditional. This override semantics and cross-parameter relationship goes beyond what the flat schema descriptions provide.

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 first sentence states a precise verb+resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It defines scope (same outcome, cross-venue) and clearly distinguishes from neighboring siblings like polymarket_arbitrage and polymarket_edges by focusing on the Kalshi↔Polymarket comparison rather than single-venue strategies. Even without opening schemas, an agent knows exactly what this tool computes.

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 explicit mode-selection guidance — TWO MODES with the 10 pre-mapped topic shortcuts listed verbatim, plus the explicit kalshi_event_ticker + polymarket_event_slug path, and states that both run the identical matcher. It also sets expectations with 'pre-mapped ≠ tradeable' and 'most pre-mapped topics return compatibility_warning today.' However, it never explicitly routes away to an alternative sibling (e.g., when to use polymarket_edges or polymarket_arbitrage instead), so it stops short of a full when-not-to-use statement.

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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