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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?

Annotations already declare readOnly/idempotent/non-destructive behavior, but the description adds substantial behavioral context: legs with unknown metric_type are never paired, pairwise matching is keyword/token-overlap based and never cryptographically verified, temporal_alignment null means 'could not compute' rather than 'aligned', and spreads are gross fees. This far exceeds the annotation signal and prepares the agent for surprising results.

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 clearly structured with labeled sections like TWO MODES, RESPONSE, and SAFETY FIELDS. Almost every sentence carries operational significance, though some repeated caveats (e.g., temporal_alignment_unknown warnings) could be trimmed without loss.

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?

For a tool with no output schema, the description is unusually complete: it names response fields, explains compatibility codes, defines null semantics, describes skipped comparison counters, and includes a fee disclaimer. This is enough for an agent to understand what the tool returns, how to interpret flags, and what cautions apply.

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?

The input schema already documents all three parameters with examples and 100% coverage. The description adds useful relationship semantics: topic mode auto-fetches matching events, the explicit ticker/slug override the mapped side, and both modes run the same matcher. This goes beyond the schema but does not add much parameter-format detail not already present.

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 states a specific purpose: computing the cross-venue probability spread between Kalshi and Polymarket for the same resolving question. It clearly distinguishes the tool's scope from siblings by emphasizing same-question matching across two specific venues, and it also explains the two invocation modes.

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 guidance on when to use the `topic` shortcuts versus explicit `kalshi_event_ticker` + `polymarket_event_slug` pairings. It also warns that most pre-mapped topics currently return compatibility_warning and are not necessarily tradeable. However, it does not explicitly name alternative sibling tools or state when NOT to use this tool in favor of them.

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

The ask_pipeworx family (stable, beta, grounded) are nearly identical, with beta explicitly matching stable, creating clear misselection risk. The five polymarket_* tools and several research tools (deep_research, bet_research, entity_profile) also overlap in purpose despite detailed descriptions.

Naming Consistency3/5

Tool names are mostly snake_case and readable, with consistent prefixes (ask_pipeworx_, polymarket_, easypost_), but mix verb-first (validate_claim, resolve_entity) and noun-first (entity_profile, ai_visibility_check) conventions. The server name 'Easypost' does not align with the overwhelmingly Pipeworx-focused tool set.

Tool Count2/5

33 tools is a heavy count, especially with three near-duplicate ask_pipeworx variants and many meta-tools. The set is also unfocused: only two shipping tools under an 'Easypost' label while the rest are a broad data-research and prediction-market platform, making the count feel bloated for the apparent scope.

Completeness2/5

As an Easypost server, shipping coverage is severely incomplete (rates and tracking only, no label purchase, address verification, or refunds). Within the Pipeworx tools, the cited pipeworx:// URIs have no direct fetch-by-URI tool, leaving a notable dead end for agents trying to retrieve full records.