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Victorian Complaint

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

A4.3/5.0
Behavior5/5

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

The description goes far beyond the readOnly/openWorld/idempotent annotations by disclosing real behavioral traits: both modes run the same token-overlap matcher, pairing_unverified is always set, compatibility warnings can be non-empty even when matched_pairs>0, Kalshi fees are not modeled, and unknown legs are never paired. This level of transparency is exceptional.

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 densely structured with bolded labels and no filler. Every caveat contributes to safe invocation, though the sheer volume of safety-code detail makes it heavier than strictly necessary.

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 return semantics, and it does so thoroughly: leg-by-leg prices, top_spreads_pp, compatibility codes, per-entry flags, skipped-leg counters, temporal alignment semantics, and the fees disclosure. An agent has enough context to call and interpret results 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?

The input schema already describes all three parameters with 100% coverage, so the baseline is 3. The description adds meaning by explaining how topic shortcuts auto-fetch events, how explicit tickers/slugs override the topic-mapped side, and that both modes use the identical matcher — genuine value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific function and resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' This makes the core purpose clear, but the description does not explicitly distinguish itself from the sibling tool polymarket_arbitrage, so it stops short of full sibling differentiation.

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 operational guidance: two modes, a topic shortcut list, explicit kalshi_event_ticker + polymarket_event_slug pairing, and a warning that 'pre-mapped ≠ tradeable.' It does not explicitly name alternatives or state when another sibling should be used instead, but the usage context is unambiguous.

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

A3.6/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily: three ask_pipeworx variants, five polymarket_* tools, and two AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence) could easily be misselected. While descriptions are detailed, the boundaries between search/research/bet/compare tools are blurry enough to cause agent confusion.

Naming Consistency4/5

Tool names follow a consistent lowercase snake_case pattern, and most use a verb-first or noun-based descriptive style (ask_pipeworx, bet_research, entity_profile, validate_claim). Minor deviations like deep_research or process_v2 are absent here; the set is largely predictable and readable.

Tool Count2/5

At 32 tools, the server exceeds the 25-tool threshold for heaviness. Many tools are edge-case variants or meta-features (pipeworx_feedback, pipeworx_trending, suggest_questions) that could be consolidated. The server's stated identity as 'Victorian Complaint' also clashes with this scale, making the count feel excessive for the apparent core purpose.

Completeness4/5

The tool set provides broad coverage for data research, entity resolution, comparison, memory, subscriptions, and prediction-market analysis. It supports query, research, discover, validate, and monitor workflows with few dead ends. Minor gaps like a generic 'get_entity' or direct data-writing tools exist, but they are not core to the implied domain.

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