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diceuear

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

A4.5/5.0
Behavior5/5

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

The description extensively discloses behavior beyond the readOnly/idempotent/non-destructive annotations: compatibility_warning and compatibility_codes can be non-empty even when matched_pairs>0, temporal_alignment_unknown is explicitly 'NOT the same as confirmed-aligned,' unclassified legs are never paired, flagged codes like pairing_unverified are explained, and every spread_pp is gross because Kalshi fees are not modeled. It also clarifies skipped_cross_type/subtype counters. This is exemplary transparency for a complex tool.

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

Conciseness3/5

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

The description is a very long, dense paragraph with many clauses and code lists. It is front-loaded with the core purpose, but readability suffers from lack of bullet points or section breaks. Every sentence carries information, but the wall-of-text format makes it harder for an agent to quickly extract the two modes, the safety fields, and the caveats.

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 there is no output schema, the description fully carries the burden of explaining return values: it details leg-by-leg prices, spread[].top_spreads_pp, compatibility_codes and their meanings, skips and skipped_* counters, temporal_alignment semantics including null handling, and the fees_note disclosure. Nothing an agent needs to interpret the response correctly 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?

Input schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining that `topic` maps to 10 pre-mapped shortcuts, that explicit parameters 'override the topic-mapped' side, and that both modes run the identical token-overlap matcher. This enriches the agent's understanding of how the three optional parameters relate to each other.

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 scopes the tool's output (leg-by-leg prices plus spread[].top_spreads_pp) and distinguishes it from a net tradeable edge by stating it does not model Kalshi fees. This differentiates it from siblings like polymarket_arbitrage without needing to name them.

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 explicitly presents two usage modes (topic shortcuts vs explicit ticker/slug pairings) and tells the agent when each is appropriate, including that explicit pairings are for 'custom pairings.' It also warns that 'most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable,' which guides when results should not be trusted. It does not explicitly name alternative sibling tools, so it stops short of full when-to-use-vs-alternatives guidance.

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

A4.1/5.0
Disambiguation4/5

Tools have mostly clear distinctions: ask_pipeworx variants differ by grounding/evidence guarantees, and meta-tools (discover_tools, suggest_questions) serve onboarding. However, ask_pipeworx_beta currently matches ask_pipeworx exactly, creating transient ambiguity, and deep_research vs ask_pipeworx overlap in routing capability though with different scopes.

Naming Consistency4/5

All tools use snake_case and most follow verb-first naming (ask_pipeworx, compare_entities, generate_avatar, subscribe). A few are descriptive nouns (recent_alerts, recent_changes, pipeworx_trending) but still readable and predictable. No mixed conventions; overall consistent style.

Tool Count2/5

33 tools is excessive for a single server, and many are auxiliary (avatar generation, memory, feedback) that do not serve the core data-access purpose. The primary question-answering capability is centralized in a few routers, making many separate tools feel redundant or unrelated, which dilutes navigability.

Completeness4/5

The core domain of structured data access is well covered with routing, grounded answering, deep research, entity profiles, comparisons, and claim validation. Subscription lifecycle (subscribe/unsubscribe/alerts) and memory (remember/recall/forget) round out the surface. Minor gaps like lack of direct tool invocation outside the router are covered by discover_tools, and no critical dead ends exist for typical queries.

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