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Ted Eu

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 readOnlyHint, openWorldHint, and idempotentHint, and the description aligns with these. It adds extensive behavioral context: compatibility flags can be non-empty even when pairs are returned, unknown metric_type or match_subtype legs are never paired, null temporal_alignment means 'could not be computed' rather than aligned, and the fee note clarifies spreads are gross. It also discloses that pre-mapped topics often return warnings, making the tool's limitations explicit.

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 but well-structured with uppercase section labels (TWO MODES, RESPONSE, SAFETY FIELDS, Codes) and dense, useful content. Each sentence adds information, though some redundancy exists — e.g., 'read them even when matched_pairs>0' is repeated and the compatibility code list is lengthy. It could be tightened but remains appropriately detailed for a tool with many edge cases.

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 must explain return values, and it does thoroughly: leg-by-leg probabilities, top_spreads_pp (Kalshi − Polymarket), compatibility_warning, compatibility_codes[], skipped_unclassified, low_confidence_pairs[], temporal_alignment, and skipped_cross_type/cross_subtype counters. It also explains null semantics and the fee disclosure. The level of detail is sufficient for an agent to invoke the tool and interpret its output 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 each parameter has a description, but the tool description adds interaction semantics: topic auto-fetches the matching event on each venue, explicit ticker/slug override the topic-mapped side, and both modes run the identical token-overlap matcher. This clarifies how the parameters relate and affect results beyond the schema's individual descriptions.

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 opening sentence states the exact function: "Cross-venue spread between Kalshi and Polymarket for the same resolving question." The two operating modes (`topic` and explicit ticker/slug) and the response shape (leg prices plus top_spreads_pp) are clearly defined. This distinguishes it from sibling tools like polymarket_arbitrage or polymarket_edges by specifying the cross-venue comparison.

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 when to use each mode: pre-mapped topic shortcuts for quick comparison, or explicit event ticker/slug for custom pairings. It also warns that "most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable," setting expectations for real-world usage. It does not explicitly name sibling alternatives or state when not to use the tool, but the mode-selection guidance and caution are clear.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose described in detail. Even tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by hallucination resistance, account requirements, and use cases. Polymarket tools are each specialized (arbitrage, edges, fill risk, etc.). No two tools appear to do the same thing.

Naming Consistency5/5

All tool names follow snake_case consistently. They use descriptive verb-noun patterns (e.g., ask_pipeworx, bet_research, compare_entities, subscribe). No mixing of conventions or ambiguous names.

Tool Count4/5

32 tools is on the higher side but justified by the server's broad scope covering company research, prediction markets, data lookups, memory, subscriptions, and more. Each tool serves a distinct function, though the count might feel slightly heavy for a single server.

Completeness4/5

The toolset covers core workflows for company analysis, prediction market operations, data retrieval, and system management (memory, subscriptions). Minor gaps exist (e.g., no direct tool for non-company entity profiles beyond drugs), but the overall surface is comprehensive for the intended multi-purpose assistant.

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