Skip to main content
Glama

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

A4.4/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, and non-destructive, and the description adds substantial behavior beyond that: it discloses the token-overlap matching limitations, gross-vs-net fee handling, the meaning of null temporal_alignment, and the fact that compatibility warnings can be non-empty even when pairs are returned.

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 it is well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS, Codes) and every sentence carries a distinct caveat or semantic fact. For a tool with no output schema and many failure modes, the density is justified rather than redundant.

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?

Given no output schema, the description fully compensates by defining response contents, safety fields, compatibility codes, skipped counters, fee treatment, and alignment semantics. An agent has enough information to invoke the tool correctly 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%, so the schema already documents all three parameters. The description adds meaningful context: it lists the topic values, states that explicit tickers override the mapped side, and clarifies that both modes run the identical matching logic with the same disclosures.

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 clearly defines the tool as computing a cross-venue spread between Kalshi and Polymarket for the same resolving question, with two explicit modes. It distinguishes this from single-venue or arbitrage-focused siblings by naming the exact comparison being performed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives strong guidance for choosing between its own two modes (topic shortcuts vs explicit tickers) and cautions that pre-mapped topics are often not tradeable. However, it never explicitly says when to choose this tool over sibling tools like polymarket_arbitrage, and it lacks exclusionary guidance for alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

The set mixes several unrelated domains (LibriVox, Pipeworx data lookup, Polymarket betting, memory, subscriptions), and within those domains there is heavy overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route to the same 5,798 tools, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk/bet_research all scan prediction-market opportunities. An agent can easily pick the wrong tool when the same question fits several of them.

Naming Consistency3/5

Names are mostly lowercase snake_case, but the patterns are inconsistent across domains: some are verb-first (ask_pipeworx, compare_entities, generate_llms_txt), some are noun-first (audiobook, tracks, polymarket_edges), and pluralization varies (audiobook vs audiobooks, authors vs tracks). The Pipeworx family is internally consistent, but the overall set has no unified convention.

Tool Count2/5

35 tools is heavy for a server named Librivox, and only 4 of them (audiobook, audiobooks, authors, tracks) actually relate to LibriVox. The remaining ~31 tools (Pipeworx, Polymarket, memory, subscriptions, AI visibility) make the count far exceed what the server name and apparent purpose suggest.

Completeness2/5

For a LibriVox-focused server, the tool surface is thin: search and fetch audiobooks, search authors, and list tracks, but no browse by genre, no reader/search-by-reader, no language filter, no author detail endpoint. The Pipeworx side is quite comprehensive, but it does not make up for the gap relative to the server's stated identity.