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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?

The description richly discloses behavior beyond the readOnlyHint/openWorldHint/idempotentHint annotations: it explains that compatibility fields can be non-empty even with matched pairs, that pairing is always unverified, that fee modeling is absent and spreads are gross, and how unclassified legs are handled. This gives the agent a clear model of the tool's limitations and caveats.

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 the tool is genuinely complex and has no output schema, so the detail is largely warranted. It is not perfectly structured — it reads as a wall of text rather than resting on bullets or clear sections — but every sentence carries substantive information about modes, safety fields, or limitations.

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 the return contract, and it does so thoroughly: leg-by-leg prices, top_spreads_pp, compatibility_codes, temporal_alignment, skipped counters, low_confidence_pairs, and fees_note are all described. The agent has enough information to invoke the tool correctly and interpret its results.

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 3, but the description adds meaningful behavior: it explains that topic auto-fetches matching events, that explicit tickers override the mapped side, and that both modes run the same token-overlap matcher. This adds semantic context beyond the raw parameter names and enum-like list.

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 statement of what the tool does: it computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. It clearly explains the two modes and what the response contains, making the tool's role obvious and distinct from generic price or arbitrage 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 strong context for when to use this tool: it contrasts the two modes, explains when explicit tickers are needed, and warns that most pre-mapped topics currently return compatibility warnings. It does not explicitly name sibling tools or state when to choose this over alternatives like polymarket_arbitrage, so it stops short of full exclusionary guidance.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ only in mode; several Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, bet_research) target related opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. The meta-tools (discover_tools, suggest_questions, pipeworx_trending) could also be confused for one another.

Naming Consistency3/5

All names are lowercase snake_case, which is consistent, but patterns vary: some are verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun (news, crypto_prices, stock_metadata), and several use brand prefixes (pipeworx_*, polymarket_*). This mixed convention is readable but not predictable.

Tool Count2/5

35 tools is too many for a coherent, well-scoped server. The set bundles a financial data API (Tiingo) with a generic data router (ask_pipeworx), prediction-market tools, memory utilities, subscription management, and npm checks — many unrelated to the server's apparent purpose, making it feel bloated.

Completeness2/5

For a Tiingo server, core data coverage is limited to stock prices, stock metadata, crypto prices, and news — missing real-time quotes, fundamentals, forex, technical indicators, and other typical Tiingo endpoints. Conversely, the general Pipeworx platform has broad query/research/subscription coverage but that domain doesn't align with the server name, leaving significant functional gaps.