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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

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, and the description adds substantial behavioral detail beyond that: the token-overlap matching method, the pairing_unverified flag, unclassified legs never being paired, the gross-not-net fee disclosure, and the skipped-comparison counters. This is exactly the kind of non-obvious runtime behavior an agent needs to interpret results correctly.

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 and dense, but it is well structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS, Codes) and every sentence carries substantive information about matching, safety flags, fees, or skipped comparisons. It is front-loaded with the core purpose, though it could be tightened slightly without losing essential caveats.

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?

Given that there is no output schema and the tool has complex matching semantics, the description is remarkably complete: it covers modes, parameter interplay, response fields, compatibility codes, skipped outcomes, temporal alignment semantics, fees, and the important warning that pre-mapped topics are frequently not tradeable. Nothing needed to invoke or interpret the tool correctly appears to be 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%, and the description adds meaningful semantics beyond the schema by explaining the relationship between the three parameters: topic is the pre-mapped mode, while kalshi_event_ticker and polymarket_event_slug override the mapped sides, and both modes run the same matcher. This clarifies parameter choice and interaction beyond the bare field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific resource and action: computing the cross-venue probability spread between Kalshi and Polymarket for the same resolving question. It does not use an explicit verb like 'compute' or 'list', and it does not name the closely related sibling tools (e.g., polymarket_arbitrage, polymarket_edges), so sibling differentiation is left implicit. Overall, the purpose is unambiguous and specific.

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 provides clear context for when to use the tool and explicitly distinguishes two modes: topic shortcuts versus explicit ticker/slug pairing. It also warns that pre-mapped topics are often not tradeable and that compatibility warnings should be read. It does not explicitly say when to choose this tool over sibling alternatives like polymarket_arbitrage, 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
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (currently functionally identical), ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog with only subtle differences. The Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also substantially overlaps in purpose, and the two unrelated domains (GIS vs. data/betting) make it worse.

Naming Consistency3/5

Names are readable and form some predictable clusters (polymarket_* prefix, ask_pipeworx_* suffixes, subscribe/unsubscribe/list_subscriptions), but conventions are mixed: bare verbs (remember, forget, recall), noun_noun (layer_info, entity_profile, pipeworx_feedback), verb_noun (query_layer, search_datasets), and adjective_noun (deep_research, recent_alerts). No single pattern dominates, though nothing is chaotic.

Tool Count2/5

34 tools is well above the 25+ threshold for a heavy surface, and the count is not justified by the server's stated identity: only 3 of 34 tools (search_datasets, layer_info, query_layer) relate to ArcGIS Glasgow. The remaining 31 tools belong to several unrelated domains (Pipeworx data querying, Polymarket betting, AI visibility, memory, npm scanning), making the effective scope far too broad.

Completeness2/5

For the server's named ArcGIS Glasgow domain, the surface is thin: search, schema inspection, and query are present, but there is no way to list all datasets, no spatial querying, and no write/update capability. Meanwhile the 31 non-GIS tools create a sprawling second server's worth of functionality, so the set as a whole has no coherent domain whose coverage can be judged complete.