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by io.github.pipeworx-io

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

A4.3/5.0
Behavior5/5

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

Annotations only signal readOnly, idempotent, openWorld, and non-destructive. The description adds substantial behavioral disclosure: matching is keyword/word-overlap based and not verified against a shared resolution source, unclassified legs are never paired, temporal_alignment null means 'unknown' rather than 'aligned', Kalshi fees are not modeled so spread_pp is gross, and compatibility warnings can appear even when pairs are returned. No contradiction 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.

Conciseness4/5

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

The description is front-loaded with purpose and uses clear internal labels like 'TWO MODES', 'RESPONSE', and 'SAFETY FIELDS,' making a long text navigable. It is somewhat repetitive across the compatibility-code list and per-entry flags explanation, so it is not maximally concise, but every major section does earn its place.

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?

There is no output schema, so the description fully shoulders the burden of explaining the return shape. It covers top_spreads_pp, compatibility_codes, skipped_unclassified, low_confidence_pairs, temporal_alignment semantics, fees_note, and skipped-cross counters, and even defines each compatibility code. An agent has enough context to invoke the tool and interpret responses 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 schema already documents the topic list and override semantics well; baseline is 3. The description adds value by framing topic as a pre-mapped shortcut layer, confirming both modes run the identical token-overlap matcher, and clarifying that custom pairings produce the same disclosures. The ticker/slug parameters themselves gain little beyond the schema, keeping this at 4 rather than 5.

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 opens with 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' which clearly identifies the resource and the core computation. It expands on this with two modes and the outcome-level matching scope, though it never explicitly names or differentiates itself from sibling tools like polymarket_arbitrage or polymarket_edges.

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 'TWO MODES' section explicitly tells an agent when to use pre-mapped topic shortcuts versus explicit ticker/slug pairing, and warns that most pre-mapped topics currently return compatibility warnings. It does not include an explicit 'when not to use this tool' statement against sibling tools, so it falls just short of full marks.

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 tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are three variants of the same router (beta is currently identical), while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. ai_visibility_check is effectively a single-entity version of scan_competitor_ai_presence, and discover_tools overlaps heavily with suggest_questions.

Naming Consistency3/5

Tool names are uniformly snake_case and descriptive, but the pattern is mixed: verb-first names (ask_pipeworx, compare_entities, search_within) coexist with noun-first names (patent, scholarly, entity_profile), and the Lens.org pairing of patent/patents_search vs scholarly/scholarly_search is structurally inconsistent. Subgroups like pipeworx_* and polymarket_* are internally consistent, keeping the overall set readable.

Tool Count2/5

At 35 tools, this exceeds the 16-25 'heavy' band and bundles at least four distinct domains: Lens.org bibliometrics, Pipeworx data routing, Polymarket trading analysis, and memory/subscription utilities. While many tools serve legitimate purposes, the set feels sprawling and several variants (e.g., the three ask_pipeworx flavors) inflate the count without adding equivalent value.

Completeness4/5

The Pipeworx/Polymarket ecosystem is thoroughly covered with routing, grounded answers, deep research, entity resolution, validation, comparison, monitoring, and memory all present. The Lens.org portion is thin (search + fetch for patents and scholarly works) but covers the core read path; minor gaps exist such as no batch/export operations and no patent-number lookup.

Resources