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Take The Meeting

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?

This description goes far beyond the annotations, which already mark the tool as read-only, idempotent, and open-world. It exposes the full safety model: compatibility codes, unverified pairing, skipped legs, temporal alignment uncertainty, gross-vs-net fee caveats, and the fact that warnings can be non-empty even when matched_pairs > 0. This is exemplary behavioral disclosure.

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 the tool is genuinely complex and the length is largely earned by the dense caveats and return-field explanations. It is front-loaded with the core purpose and modes before diving into safety details. Some repetition and block-cap-heavy presentation could be tightened, but no sentence feels gratuitous.

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 values, and it does so thoroughly: venue prices, top_spreads_pp, compatibility fields, skipped categories, low-confidence pairs, temporal alignment, fees, and cross-type/cross-subtype counters. An agent has enough information to correctly invoke the tool 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 input schema already documents all three parameters, so the baseline is 3. The description adds real semantic value by explaining that topic auto-fetches a matching event, that the explicit ticker and slug override the topic-mapped sides, and that both modes execute the identical token-overlap matcher with the same disclosures. This meaningfully exceeds schema-only understanding.

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 states the tool's core function precisely: computing the cross-venue probability spread between Kalshi and Polymarket for the same resolving question, and it clearly differentiates the two invocation modes. It is unambiguous and rich, though it does not explicitly name a sibling tool it is not, relying instead on the reader to infer the distinction from "not a net tradeable edge."

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 two modes are clearly explained: topic shortcuts for pre-mapped macros and explicit ticker/slug for custom pairings, with the note that both run the same matcher. It also gives important when-not-to-trust guidance ("pre-mapped ≠ tradeable", "most pre-mapped topics return compatibility_warning today"), but it does not explicitly discuss alternatives among sibling tools.

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

A3.7/5.0
Disambiguation3/5

Most tools have distinct, well-documented roles, but the set includes two effectively identical routers (ask_pipeworx and ask_pipeworx_beta, with the latter explicitly matching the former right now) and a dense cluster of six Polymarket tools that an agent must read carefully to choose among. This is more than a minor overlap, though the detailed descriptions prevent it from being complete chaos.

Naming Consistency3/5

Everything is snake_case, which is a plus, but the patterns are inconsistent: some tools are verb_noun (resolve_entity, validate_claim, list_subscriptions), some are noun phrases (entity_profile, bet_research, recent_alerts), some use brand prefixes (ask_pipeworx*, polymarket_*, pipeworx_*), and take_the_meeting_evaluate is a sentence-like outlier. The prefixes do provide grouping, but the naming doesn't give a predictable action structure.

Tool Count2/5

32 top-level tools is above the 25+ threshold and will bloat an agent's tool-selection surface, especially because the server name 'Take The Meeting' suggests a narrow meeting tool while 31 of the tools are unrelated Pipeworx/data features. Even as a broad data-research server, several tools could be collapsed (the ask variants, the Polymarket family), so the count feels inflated.

Completeness2/5

For the purpose implied by the server name and the lone meeting tool, the surface is severely incomplete: there is no way to list or fetch meetings, access calendar/attendee context, or do anything beyond evaluating one set of supplied parameters. For the broad data-research domain that most tools actually serve, the read/research side is rich, but that is a completely different purpose from 'Take The Meeting', leaving the meeting feature as a disconnected dead end.

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