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uselessfacts

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior; the description adds substantial non-obvious behavioral context: comparisons are keyword-based and always `pairing_unverified`, Kalshi fees are not modeled so spreads are gross, unknown legs are never paired, and temporal_alignment null has a distinct meaning. This goes far beyond the 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 purpose is front-loaded and the safety-related explanation is structured with clear labels like RESPONSE, SAFETY FIELDS, and Codes. It is long, but nearly every sentence carries essential interpretation guidance for a tool with no output schema; only minor redundancy keeps it from a 5.

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 so thoroughly: it covers top_spreads_pp, per-entry flags, compatibility_codes, skipped counters, temporal_alignment semantics, and fee treatment. An agent has enough information to call the tool and interpret its results safely.

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% and the schema already describes each parameter and override behavior. The description adds meaningful context by explaining that `topic` auto-fetches the matching event on each venue and that both modes use identical matching with identical disclosures, enriching rather than merely repeating 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 defines exactly what the tool does: it computes a cross-venue spread between Kalshi and Polymarket for the same resolving question. It adds meaningful specifics beyond the title — the 2-25pp divergence, participant-pool cause, and equivalence caveat — and the cross-venue scope clearly separates it from single-venue siblings.

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 clearly explains the two invocation modes (`topic` vs explicit `kalshi_event_ticker` + `polymarket_event_slug`) and states that both run the same matching logic. It also warns that pre-mapped topics often return compatibility warnings, helping the agent set expectations, though it does not explicitly name alternative sibling tools or exclusion conditions.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx for general queries, ask_pipeworx_grounded for high-stakes verification, deep_research for multi-faceted research). However, some overlap exists among the ask_* variants and the prediction-market tools (bet_research vs. polymarket_edges vs. polymarket_arbitrage), which could cause misselection without careful reading of the detailed descriptions.

Naming Consistency4/5

Tool names consistently use snake_case and mostly follow the verb_noun pattern (e.g., list_subscriptions, resolve_entity, validate_claim). Minor deviations like random_fact and today_fact (adjective_noun) and pipeworx_feedback (noun_noun) introduce slight inconsistency, but the overall pattern is predictable.

Tool Count3/5

With 33 tools, the count exceeds the typical 3-15 range and even the 16-25 'heavy' threshold. While the server covers an unusually broad domain (data retrieval, prediction markets, memory, subscriptions, AI visibility), several tools could be consolidated (e.g., the six polymarket tools, trivial random_fact/today_fact). The scope partially justifies the count, but it feels over-provisioned.

Completeness4/5

The tool surface is remarkably comprehensive for a data platform: it covers querying, entity resolution, comparison, change feeds, memory persistence, subscription management, validation, and even meta-tool discovery. Minor gaps exist (e.g., no explicit update/delete for external data, but that is not the service's purpose). Overall, no obvious dead ends.

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