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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

A4.7/5.0
Behavior5/5

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

The description goes far beyond the annotations, disclosing that returned matches may still carry compatibility warnings, that legs with 'unknown' metric_type or match_subtype are never paired, that temporal_alignment null means 'could not be computed' rather than aligned, and that spreads are gross of Kalshi fees. This richly characterizes the tool's conservative matching behavior and non-tradeable edge, exceeding what readOnlyHint and idempotentHint alone would convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is long but every sentence earns its place, with clear scannable labels like TWO MODES, RESPONSE, SAFETY FIELDS, and Codes. It front-loads the core purpose and then layers critical caveats and return-field semantics without redundancy. For a tool with no output schema and multiple safety fields, this density is justified.

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?

Because there is no output schema, the description must carry the full burden of explaining return values, and it does: spread[], top_spreads_pp, flags[], compatibility_codes[], temporal_alignment, fees_note, skipped_unclassified, and low_confidence_pairs are all described. It also covers edge cases like unknown legs, temporal mismatches, and fee modeling limitations, making the tool safe and correctly invocable without additional documentation.

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?

The schema already covers all three parameters with descriptions, so the baseline is 3. The description adds meaning by explaining that explicit kalshi_event_ticker and polymarket_event_slug override the topic-mapped sides and that BOTH modes run the identical token-overlap matcher. This clarifies parameter interaction beyond the schema's simple field 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 description opens with a specific, verb-like resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It immediately distinguishes the tool's focus from Polymarket-only siblings by naming both venues and the matching concept. It also states the two modes and the exact output the agent can expect, leaving no ambiguity about what the tool does.

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 on when the spread is meaningful ('when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so') and sets expectations with 'pre-mapped ≠ tradeable.' However, it never names an alternative sibling such as polymarket_arbitrage or explains when to prefer that tool, so the guidance is strong on usage caveats but lacks explicit sibling routing.

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

A3.9/5.0
Disambiguation2/5

Several tools have near-identical purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are the same router with only an evidence-extraction difference. polymarket_edges, bet_research, and polymarket_arbitrage also overlap heavily in surfacing mispricings, and ai_visibility_check is essentially a single-entity version of scan_competitor_ai_presence.

Naming Consistency3/5

There are recognizable patterns: ask_pipeworx_*, polymarket_*, verb_noun pairs like define_word, get_synonyms, resolve_entity. However, conventions are mixed across the set — ask_pipeworx_beta uses a suffix, ai_visibility_check vs scan_competitor_ai_presence are phrased in different styles, and memory tools (remember/recall/forget) follow yet another pattern. Readable but not predictable.

Tool Count2/5

33 tools is heavy for a single server, and many of them are meta-tools (discover_tools, suggest_questions, ask_pipeworx variants, pipeworx_trending, pipeworx_feedback, memory tools) that inflate the surface. The count would be defensible if each tool were orthogonal, but the overlap in research/Polymarket/memory areas means several tools do not earn their place.

Completeness4/5

For the actual Pipeworx data-access domain, coverage is quite rich: lookup, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and discovery tools all exist. Minor gaps remain (e.g., no tool to manage account/API keys, patents soft-fail), but the core query-research-monitor lifecycle is well covered. The server name 'dictionary' is misleading — only two tools serve a dictionary purpose — yet the inferred domain from descriptions is a data gateway, for which the surface is strong.

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