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wikiviews

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 detailing compatibility_codes[], skipped unclassified legs, temporal_alignment null semantics, gross-vs-net fee disclosure, and per-pair flags. It also warns that compatibility warnings can be non-empty even when matched_pairs>0, which is valuable behavioral context.

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 organized: purpose, modes, response format, safety fields, and final caveat. It front-loads the core function and uses clear labels like 'TWO MODES', 'RESPONSE', and 'SAFETY FIELDS'. Some sentences are repetitive, but every major clause serves a real disclosure or usage purpose.

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, the description compensates thoroughly by explaining response components: top_spreads_pp, compatibility_warning/codes, temporal_alignment, skipped_cross_type/subtype, fees_note, and low_confidence_pairs. It covers what the agent needs to correctly interpret results and avoid treating gross spreads as net tradeable edges.

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 covers all three parameters at 100%, so the baseline is 3. The description adds meaningful semantics: topic auto-fetches matching events, explicit ticker/slug override the mapped sides, and both modes run the same matcher. This exceeds the schema-only baseline without being redundant.

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 description clearly identifies the tool as computing the cross-venue spread between Kalshi and Polymarket for the same resolving question, with specific phrases like 'leg-by-leg prices' and 'Kalshi − Polymarket'. It does not explicitly differentiate itself from sibling tools such as polymarket_arbitrage, so it misses the highest bar.

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 usage context: two modes ('topic' shortcuts vs explicit ticker/slug pairings), what each mode does, and warnings like 'pre-mapped ≠ tradeable' and 'most pre-mapped topics return compatibility_warning today'. It does not explicitly describe when to prefer this over sibling tools, but the mode-level guidance is strong.

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
Disambiguation5/5

Every tool has a clearly distinct purpose, from Wikipedia page views to AI visibility checks, entity resolution, and Polymarket betting. No two tools appear overlapping in functionality; descriptions further clarify each tool's unique role.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., get_article_views, subscribe, resolve_entity). No mixing of conventions, and names are descriptive enough to infer purpose.

Tool Count2/5

With 33 tools, the server is overloaded for its name 'wikiviews', which suggests a focused Wikipedia views tool. The set includes unrelated functionality like Polymarket arbitrage, memory storage, and Pipeworx data queries, making the scope feel excessive.

Completeness2/5

For the core domain of Wikipedia views, only 3 tools exist (get_article_views, get_project_views, get_top_articles), missing basic operations like list_articles_per_day. The unrelated tools are extensive, but the server's stated purpose is poorly served.

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