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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

A4.6/5.0
Behavior5/5

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

The description adds substantial behavioral disclosure beyond the readOnly/idempotent annotations: matching is keyword-based, not provenance-based ('pairing_unverified'); fees are not modeled ('every spread_pp is gross'); unclassified legs are never paired; and temporal_alignment null means 'could not be computed,' not alignment. This is exactly the kind of caveat detail an agent needs before trusting a returned spread.

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 opening sentence is front-loaded and the rest is organized with labeled sections like 'SAFETY FIELDS,' 'RESPONSE,' and 'Codes.' It is long, but the complexity of the tool justifies most of the length. A few repeated warnings (e.g., pre-mapped ≠ tradeable appearing twice) could be tightened 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?

With no output schema, the description carries the full burden of explaining return semantics, and it does: leg-by-leg prices, top_spreads_pp, compatibility_warning, compatibility_codes, skipped counters, fees_note, and temporal_alignment. Given the tool's complexity and the absence of an output schema, nothing essential for correct invocation or interpretation is missing.

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. The description adds meaning by explaining that 'BOTH modes run the identical token-overlap matcher, so the same disclosures apply to both' and that topic shortcuts auto-fetch matching events. It clarifies the relationship between the three parameters without merely restating the schema.

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 specific verb and resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly distinguishes this from single-venue analysis and the many sibling research tools. It also states the two operational modes, making the tool's function explicit rather than tautological.

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 clear context for when to use each mode: 'TWO MODES: (1) topic — 10 pre-mapped macro shortcuts... (2) explicit kalshi_event_ticker + polymarket_event_slug for custom pairings.' It also warns that 'pre-mapped ≠ tradeable' and that returned compatibility fields must be read even when matches exist. It does not explicitly name a sibling alternative to switch to, but the mode-selection guidance is strong.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.

Naming Consistency2/5

Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.

Completeness4/5

For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.

Resources