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climate

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 far exceeds the readOnly/idempotent annotations by disclosing non-obvious behavior: unmatched 'unknown' legs are never paired, compatibility codes can be non-empty even when matched_pairs>0, temporal_alignment null means unknown rather than aligned, and spread_pp is gross because Kalshi fees are not modeled. It also documents skipped_cross_type/cross_subtype counters and per-pair flags.

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 it is organized with clear labels such as TWO MODES, RESPONSE, SAFETY FIELDS, and Codes, and the core definition is front-loaded. There is some redundancy, such as repeating that both/either modes share the same matcher, so it is not maximally concise, yet each section contributes essential 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?

With no output schema, the description carries the full burden of explaining return semantics, and it does: raw probabilities, top_spreads_pp, skipped counters, compatibility codes and meanings, per-entry flags, fees disclosure, and temporal alignment semantics. An agent has enough context to call the tool correctly and interpret results without mistaking gross spreads for 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful semantics: topic is a set of pre-mapped shortcuts that auto-fetch matching events, explicit parameters override the mapped side, and both modes share the same matching and disclosure behavior. This goes beyond the schema's example values.

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 is precise: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' and the description goes on to define two modes and the response shape. It is clear and specific, but it never explicitly distinguishes this tool from sibling tools like polymarket_arbitrage, so sibling differentiation is implied rather than stated.

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 strong usage guidance: two modes ('topic' vs explicit ticker/slug), the fact that both run the identical matcher, and the warning that 'most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.' It does not explicitly name alternatives or state when this tool should not be used, but the mode-based instructions are concrete and actionable.

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 query-router tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all serve as entry points into the same underlying data, with ask_pipeworx_beta explicitly described as currently identical to ask_pipeworx. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have fuzzy boundaries that an agent could easily mis-select.

Naming Consistency3/5

Most tools follow a readable lower_snake_case verb_noun pattern (compare_entities, get_climate_projection, resolve_entity), but conventions are mixed: pipeworx_feedback, pipeworx_trending, recent_alerts, and recent_changes are noun-first/non-imperative, and the ask_pipeworx family uses a verb-plus-variant-suffix style. The pattern is predictable enough to navigate but not consistent.

Tool Count2/5

33 tools is heavy for a single MCP server, and the server name 'climate' does not match the broad data-research, prediction-market, memory, subscription, and web-utility scope actually covered. Several tools could be consolidated (ask_pipeworx variants, discover_tools/suggest_questions, multiple polymarket scanners), which would reduce cognitive load without losing capability.

Completeness4/5

As a general data-access and research surface, the tool set is quite complete: it covers lookup, grounded verification, deep research, entity resolution, comparisons, recent changes, memory, subscriptions, alerts, feedback, and tool discovery. Minor gaps exist — the climate-specific coverage is limited to projections and model comparison despite the server name, and there is no direct tool to page through the full catalog — but for its inferred broad purpose there are no major dead ends.

Resources