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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 mark it read-only and non-destructive, and the description adds substantial behavioral disclosure: the token-overlap matcher, safety codes and their meanings, the fact that unknown metric_type/match_subtype legs are never paired, the fees_note that spreads are gross, and the temporal_alignment null semantics. No contradiction with annotations.

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 deliberately organized (modes, response, safety fields, codes, fee note, counters) and front-loads the purpose. Some repetition of warnings (e.g., EITHER mode, pre-mapped not tradeable) could be trimmed, but every major section adds operational value for a complex tool.

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 no output schema and a complex response, the description fully covers return semantics, safety fields, flag meanings, fee caveat, skipped-leg counters, and limitations. An agent has enough information to call it correctly and interpret the result safely.

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. The description adds valuable semantics beyond the schema: topic is an enumerated set of macro shortcuts, explicit ticker/slug override the topic-mapped sides, and both modes run the identical matcher with the same caveats.

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 defines the tool as a cross-venue spread between Kalshi and Polymarket for the same resolving question, which clearly identifies both the resource and the computation. The two modes further scope what it operates on, and the 'same resolving question' qualifier distinguishes it from generic arbitrage or edge tools.

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 context for how to invoke it: either pick a pre-mapped topic or supply an explicit kalshi_event_ticker and polymarket_event_slug, and it warns that most pre-mapped topics currently return a compatibility_warning and that pre-mapped does not mean tradeable. It stops short of naming sibling tools or stating explicit when-not-to-use conditions, hence not a 5.

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

A3.9/5.0
Disambiguation3/5

The descriptions are extraordinarily detailed and genuinely differentiate most tools, but the set contains near-clones (ask_pipeworx, ask_pipeworx_beta which is explicitly 'identical' to it, and ask_pipeworx_grounded) plus five polymarket tools whose boundaries (arbitrage vs edges vs edge_tracker vs fill_risk vs kalshi_spread) overlap enough to cause misselection. An agent navigating this surface must read full descriptions to choose correctly, which defeats quick tool selection.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity), but there are clear deviations: bare single-word verbs (remember, recall, forget, subscribe, unsubscribe), prefix-family names (pipeworx_feedback, pipeworx_trending; polymarket_edges, polymarket_fill_risk), and a disjoint ArcGIS trio (layer_info, query_layer, search_datasets) that breaks the dominant convention. It is readable but not predictable across the whole set.

Tool Count2/5

34 tools is far beyond what the 'Arcgis Orovalley' purpose warrants — only 3 tools (search_datasets, layer_info, query_layer) actually relate to GIS, with 31 unrelated tools bolted on covering financial data, prediction markets, memory, subscriptions, and AI visibility. This is a sprawling mega-server where an agent must hold an enormous option set in mind; the surface appears to be several platforms fused together rather than one well-scoped toolset.

Completeness3/5

The genuine ArcGIS surface (search datasets → layer_info → query_layer) is a complete read-only workflow with no dead ends, and the Pipeworx side is impressively comprehensive (routing, grounded answers, research, entity, compare, resolve, validate, memory, subscriptions, feedback). But the tool set as a whole serves no single coherent domain — the declared purpose (ArcGIS Oro Valley data) lacks any write/editing operations, while the majority of the surface addresses unrelated concerns, so 'complete' only applies to one small slice of the 34 tools.