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Congress

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?

Annotations already indicate readOnly, openWorld, and idempotent behavior, but the description goes far beyond that by disclosing fee modeling limits, the meaning of null alignment, skipped-leg counters, compatibility codes, and the fact that warnings can be non-empty even when matches exist. This is rich behavioral context that prevents misuse.

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 labels like 'TWO MODES', 'RESPONSE', 'SAFETY FIELDS', and 'Codes'. It is front-loaded with the core purpose and then systematically explains edge cases and warnings. There is some redundancy around temporal_alignment_unknown, but the density is largely justified by the tool's complexity.

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 carries the full burden of explaining the response shape, including top_spreads_pp, compatibility_codes, skipped_unclassified, low_confidence_pairs, temporal_alignment, fees_note, and the skipped counters. It also addresses edge cases like unknown metric types and unparseable resolution months, making it complete for an agent to invoke 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?

Schema coverage is 100%, so the baseline is 3, and the description adds meaningful semantics beyond the schema: it explains that explicit parameters override the topic-mapped side, that both modes use an identical matcher, and how the topic shortcut list behaves in practice. This elevates it above baseline, though the schema already documents the basic parameter meanings.

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 clearly states the tool computes a cross-venue spread between Kalshi and Polymarket for the same resolving question, naming the two venues and the object of comparison. It distinguishes itself from likely sibling tools by focusing specifically on the Kalshi–Polymarket pairing and by describing two distinct modes of operation.

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 concrete usage guidance: it explains the topic shortcuts, the explicit ticker/slug mode, that both modes run the same matcher, and warns that pre-mapped topics often return compatibility warnings and are not necessarily tradeable. It does not explicitly name alternative sibling tools or state when to prefer them, so it stops short of a 5.

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

Multiple tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_ipeworx_grounded, and deep_research all answer questions; disover_tools, suggest_questions, and pipeworx_trending all serve discovery; and five polymarket_* tools plus bet_research overlap heavily on prediction-market opportunity detection. The descriptions are detailed, but the boundaries are subtle enough that an agent can easily pick the wrong tool.

Naming Consistency4/5

The set is uniformly lowercase snake_case and uses recognizable prefixes such as ask_pipeworx, polymarket_, pipeworx_, and get_, which makes the naming fairly predictable. It is not a strict verb_noun convention — some names are noun phrases like entity_profile or ai_visility_check — but the overall style is consistent.

Tool Count2/5

With 36 tools, the surface is far heavier than the 'Congress' name suggests: only five tools are actually about congressional data, while the rest are general research, memory, subscription, and meta utilities. Many of these overlap, so the count feels bloated rather than well-scoped.

Completeness3/5

For Congress-specific work, the core needs are covered: search bills, get bill details, list members, and retrieve recent votes. However, deeper legislative operations like member voting records, committee actions, and amendments are missing, and the surrounding Pipeworx tools do nothing to close that gap. As a general data-research platform it is broad, but its actual 'Congress' identity feels incompletely realized.

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