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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

A4.4/5.0
Behavior5/5

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

The description extensively discloses behavioral traits beyond the readOnly/annotations: matching algorithm ('matched by keyword and word overlap, not a shared resolution source'), fee treatment ('Kalshi charges per-contract trading fees... every spread_pp is gross'), null semantics for temporal_alignment, skipped-leg counters, and the meaning of each compatibility code. The annotation contradiction check is clean; readOnlyHint=true aligns with the read-only 'fetch/compare' behavior.

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 front-loaded and well-structured, using capital labels like 'TWO MODES:', 'RESPONSE:', 'SAFETY FIELDS:' and bullet-like enumerations. A few points are reiterated (e.g., the keyword-overlap caveat appears twice), but the density of caveats is warranted for a cross-venue comparison tool with many edge cases.

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 explain the return shape, and it does: leg-by-leg prices, top_spreads_pp, compatibility fields, temporal_alignment, fees_note, and skipped counters. It covers failure/safety semantics, mode-specific behavior, and the tool's practical reliability limitations, leaving little an agent needs to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with each parameter already having a description (topic list, override semantics). The description adds context that topic 'auto-fetch[es] the matching event' and that both modes run the same matcher, but it does not add significant new per-parameter meaning beyond the schema. Thus baseline 3 is appropriate.

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 specific verb and resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' This clearly distinguishes it from siblings like polymarket_arbitrage (same-venue) and compare_entities by naming the exact two venues and the cross-venue comparison purpose.

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 mode: 'TWO MODES: (1) topic — 10 pre-mapped macro shortcuts... (2) explicit kalshi_event_ticker + polymarket_event_slug for custom pairings.' It also warns that 'pre-mapped ≠ tradeable' and that most topics return compatibility_warning today, setting expectations. However, it does not explicitly name alternative sibling tools or state when NOT to use this tool, so it stops short of 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

A3.6/5.0
Disambiguation3/5

Most tools have distinct, well-documented purposes, but several overlap or are explicitly redundant: ask_pipeworx_beta currently behaves identically to ask_pipeworx, discover_tools and suggest_questions both serve as discovery entry points, and scan_competitor_ai_presence wraps ai_visibility_check. The thematic split between theme-park, data-lookup, prediction-market, and memory tools also forces agents to navigate unrelated clusters.

Naming Consistency3/5

Naming is a mix of verb_noun (list_destinations, get_wait_times, remember, resolve_entity), noun_phrase (entity_profile, recent_changes, bet_research), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Within families the patterns are consistent, but across the set the conventions are inconsistent and sometimes reverse the verb/noun order, making the surface harder to predict.

Tool Count2/5

35 tools is heavy, and the server is named Themeparks yet only 4 tools actually relate to theme parks. The remaining 31 tools span Pipeworx data retrieval, prediction markets, memory, subscriptions, and feedback, creating a bloated and misaligned scope. A tightly scoped theme-park server would need far fewer tools, and a general data research server would not be named Themeparks.

Completeness2/5

For the implied theme-park domain, the surface is thin: list destinations, get entity metadata, get schedule, and get wait times cover basic lookups but omit search, attraction details beyond waits, historical data, pricing, dining/show info, and park updates. The non-theme-park tools are extensive, but they do not complete the server's apparent stated purpose.