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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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds substantial behavioral context beyond those: compatibility_codes semantics, the gross-vs-net fee disclosure, the unclassified legs never being paired, and the temporal_alignment null meaning. It thoroughly discloses the matching limitations and edge cases, which is exactly what an agent needs to interpret the tool's output safely.

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 information-dense and organized by modes, response fields, safety fields, and standing disclosures. Every sentence contributes a distinct operational fact, and key warnings are front-loaded in the opening sentence and reinforced near the end. It could be tightened slightly, but it earns its length given the tool's complexity and the absence of an output schema.

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 comprehensively: raw probabilities, top_spreads_pp, compatibility_warning/codes, per-entry flags, skipped_unclassified, low_confidence_pairs, temporal_alignment, fees_note, and skipped_cross_type/subtype counters. It also explains null semantics and what is never paired. This is complete enough for an agent to invoke and interpret the tool correctly in both modes.

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 schema already documents each parameter individually. The description adds meaning beyond that by explaining how the parameters interact: `topic` auto-fetches matching events on both venues, the explicit ticker/slug override the topic-mapped sides, and both modes share the same token-overlap matcher. This clarifies execution semantics rather than just listing values, so a 4 is warranted over the baseline 3.

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 verb and object: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly distinguishes the tool's scope from related arbitrage and edge tools by emphasizing cross-venue comparison and equivalent bet shapes, so an agent can tell what this tool does without opening the schema.

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 usage guidance for both modes: when to use `topic` shortcuts vs explicit `kalshi_event_ticker` + `polymarket_event_slug`, and states that both modes run the identical matcher. It also warns that most pre-mapped topics return compatibility warnings and that pre-mapped ≠ tradeable. It does not name sibling alternatives like polymarket_arbitrage, but the within-tool routing is clear 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

A3.8/5.0
Disambiguation3/5

While many tools have detailed descriptions that help differentiate them, there is significant overlap among query tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research. The prediction market tools also cluster together, making it challenging for an agent to quickly pick the right one without careful reading.

Naming Consistency4/5

Most tools follow a descriptive snake_case convention (e.g., ask_pipeworx, entity_profile, compare_entities). Minor deviations exist, such as 'ai_visibility_check' and 'deep_research', but overall the naming pattern is predictable and clear.

Tool Count3/5

With 33 tools, the server is larger than typical single-domain servers. While it supports a broad data platform, this count feels somewhat bloated and could benefit from consolidation, especially among overlapping query tools.

Completeness1/5

The server is named 'Materials' but contains only two materials-specific tools (materials_search, materials_stability). The remaining 31 tools cover unrelated domains (finance, economics, prediction markets, etc.), leaving the stated domain severely incomplete.