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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.

TDQS

A4.3/5.0
Behavior5/5

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

Even though annotations already declare readOnly/openWorld/idempotent/non-destructive behavior, the description adds unusually rich behavioral detail: unknown legs are never paired, temporal_alignment null semantics are clarified, the fee caveat is explicit (gross, not net tradable edge), and compatibility code meanings are enumerated. This goes far beyond annotation signals.

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 dense and front-loaded with the core purpose, and almost every sentence carries safety or semantics information needed by the caller. It is long and repeats some cautions (pre-mapped ≠ tradeable, compatibility warnings may be present), so a little tightening would be possible without losing value.

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?

This is a complex tool with no output schema, and the description supplies the missing return semantics: leg prices, top_spreads_pp, compatibility fields, skipped counters, fee note, and per-entry flags. An agent has enough information to call the tool in either mode and correctly 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, and the description adds real value by explaining how topic vs explicit ticker/slug modes behave and that both run the identical token-overlap matcher. It also enumerates the valid topic shortcuts, though it does not add much per-parameter format detail beyond the schema examples.

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 the resource as a cross-venue spread between Kalshi and Polymarket for the same resolving question, and it defines two distinct invocation modes. It does not explicitly name sibling tools like polymarket_arbitrage to differentiate from them, so it stops short of full sibling-level distinction.

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 explicit mode-selection guidance: use the pre-mapped topic shortcuts or provide explicit ticker/slug overrides, and states that both modes run the same matcher. It also cautions that most pre-mapped topics currently return compatibility warnings, but it does not mention when to prefer alternative sibling tools.

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
Disambiguation3/5

The set mixes several overlapping clusters: three ask_pipeworx variants (beta is explicitly identical to stable right now), six Polymarket tools with similar opportunity-scanning purposes, and two AI-visibility tools that duplicate each other. However, the descriptions are detailed enough that an agent can usually pick correctly, so the ambiguity is moderate rather than severe.

Naming Consistency2/5

Tool names follow no single convention — some are verb_noun (query_layer, validate_claim), some noun_noun (entity_profile, layer_info), some company-prefixed clusters (pipeworx_*, polymarket_*), and a few standalone verbs (forget, recall). While snake_case is consistent, the absence of a uniform verb_noun pattern across the set makes it unpredictable.

Tool Count2/5

At 34 tools, the server is oversized for its apparent purpose, and the count is even more problematic because most tools belong to a general Pipeworx/data platform while only 3 serve the 'Arcgis Princewilliam' GIS theme. The set feels like two unrelated servers merged, with many tools earning no clear place in a unified product.

Completeness2/5

The GIS side is a read-only stub (search, schema, and query) with no editing or feature-level retrieval, and the broader Pipeworx side has a notable dead end: tools return pipeworx:// citation URIs but no tool is provided to fetch those resources. The result is a surface that is simultaneously over-built in prediction markets and under-built in its namesake domain.