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musicurainz

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?

Even though annotations already mark the tool read-only and non-destructive, the description adds substantial behavioral context: identical matcher in both modes, compatibility codes that can be non-empty even with matched pairs, fees not modeled, spreads are gross, skipped comparisons, and limitations of pre-mapped topics. This goes well beyond the annotations and gives an agent realistic expectations.

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 clear section labels (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded purpose. Some content is repeated across prose and code lists, but given the complexity and safety-critical caveats, the length is largely 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?

With no output schema present, the description carries the full burden of explaining the return shape, and it does so thoroughly: leg prices, top_spreads_pp, compatibility_codes, flags, temporal_alignment, fees_note, skipped counters, and low_confidence_pairs. An agent has enough context to invoke the tool correctly and interpret its results.

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 documents all three parameters at 100% coverage, so the baseline is 3. The description adds meaningful extra semantic context by explaining that topic auto-fetches matching events, that explicit parameters override the topic-mapped sides, and that both modes run the same matcher. It also supplies concrete examples of valid ticker and slug formats.

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 states a specific, identifiable purpose: computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It clearly distinguishes itself from sibling Polymarket tools by emphasizing the cross-venue scope and by detailing two concrete operating modes.

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 explains when to use topic shortcuts versus explicit event ticker/slug pairings, and warns that pre-mapped topics often return compatibility warnings and are not automatically tradeable. It does not explicitly name sibling alternatives like polymarket_arbitrage or polymarket_edges, so exclusions rely on context rather than direct comparison.

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

A3.8/5.0
Disambiguation3/5

Many tools are crisply separated (memory CRUD, subscription lifecycle, single-entity vs compare vs profile), but several broad entry points overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very close variants, and discover_tools/suggest_questions/ask_pipeworx all serve discovery/routing. Descriptions help, but an agent can still easily select one of the duplicate or adjacent tools instead of the intended one.

Naming Consistency3/5

All names are readable snake_case and there are coherent families (polymarket_*, ask_pipeworx_*, search_*, get_*), but there is no consistent verb_noun convention: noun-phrase names like recent_alerts and pipeworx_trending coexist with single verbs like remember and forget and domain-prefixed nouns like polymarket_edges. Mixed, but still reasonably navigable.

Tool Count2/5

At 35 tools, the surface is well past the comfortable 3-15 tool scope and even beyond the 16-25 heavy range unless the server has one explicit mega-purpose. The set also sprawls across music lookup, Pipeworx research, prediction markets, npm checks, LLM visibility, memory, and subscriptions, so no single coherent job emerges.

Completeness4/5

For the dominant read-only research workflow, the set is remarkably complete: discover tools, grounded and ungrounded asking, deep research, entity resolution, profiles, recent changes, comparisons, claim verification, search_within, plus full memory and subscription lifecycles. Minor gaps include the shallow music side relative to the rest of the server and the absence of an explicit source catalog.

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