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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. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "topic": "fed"
      +  },
      +  {
      +    "topic": "btc"
      +  }
      +]
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already declare this read-only and idempotent, and the description adds substantial behavior beyond that: compatibility codes, gross-vs-net fee disclosure, unclassified-leg exclusion, temporal alignment semantics, and the warning that safety fields can be non-empty even when pairs are returned. No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but densely packed with essential caveats, response-field explanations, and operational semantics. It is front-loaded with the core purpose, then modes, then safety disclosures, and ends with an honest rarity warning. Every section earns its place given there is no output schema to lean on.

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 fully explain return values, and it does: leg-by-leg prices, top_spreads_pp with per-entry flags, compatibility codes, temporal alignment, skipped counters, and the fees note. It also handles edge cases like unknown legs never being paired and null temporal alignment meaning unknown rather than aligned. This is complete enough for an agent to call and interpret the tool correctly.

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 baseline is already solid. The description adds meaning by explaining the `topic` enum's semantic role as pre-mapped macro shortcuts, that explicit ticker/slug parameters override the mapped side, and that both modes share the same matching logic. This is useful but mostly confirms what the schema already communicates.

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 names the exact resource (cross-venue spread between Kalshi and Polymarket) and the operation (computing the delta in probability for the same resolving question). It also details two input modes and the output structure, clearly distinguishing its Kalshi–Polymarket focus from sibling trading 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 clearly explains when to use `topic` shortcuts vs explicit `kalshi_event_ticker`/`polymarket_event_slug` pairing, and notes that both modes run the same matcher. It warns that pre-mapped topics are not automatically tradeable, but it never explicitly contrasts this tool with alternatives like polymarket_arbitrage or polymarket_edges, so the when-not-to-use guidance is incomplete.

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
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve queries, with ask_pipeworx_beta explicitly identical to ask_pipeworx. Prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research, etc.) also have unclear boundaries, making tool selection tricky for an agent.

Naming Consistency4/5

Tool names are consistently lowercase snake_case with a mostly verb-first pattern (get_work, search_works, list_subscriptions, validate_claim). Minor deviations exist: some names are noun-first (entity_profile, recent_changes) and prefixes vary (get/search/list/ask/scan), but the overall convention is predictable and readable.

Tool Count2/5

34 tools is far too many for a server named 'crossref', and only three tools actually relate to Crossref. The rest form a sprawling utility belt covering data routing, memory, subscriptions, prediction markets, AI visibility, and npm scanning — a scope mismatch that makes the server feel like a kitchen sink rather than a focused offering.

Completeness2/5

The Crossref-specific surface is thin: search_works, get_work, and get_journal cover discovery and metadata but lack citation lookup, author search, and funder information. The broader Pipeworx surface is extensive but has no unifying domain, so it's impossible to consider the overall toolset complete for any coherent purpose.