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compare_executable_quotes

Fetch size-aware order-book fills and estimated taker fees for one Polymarket contract and one Kalshi contract, then compare their complete rules. It only names a best venue when the pair has been explicitly human-reviewed as equivalent. Similar titles with different settlement criteria return comparable: false plus blockers instead of a fabricated spread or arbitrage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNoYes
amount_usdNo
kalshi_tickerYes
polymarket_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It honestly reveals that it will not fabricate a spread or arbitrage and returns a blocker object for non-equivalent pairs. It does not mention auth, rate limits, or error handling, but the specified edge-case behavior is substantial and non-obvious, which surpasses most descriptions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, with no filler. The first sentence states the core purpose, the second adds the key limitation, and the third clarifies a return behavior. Every sentence earns its place, and it is front-loaded effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex (comparison across two platforms) and outputs structured data (output schema exists), so the description appropriately focuses on behavior and constraints. It covers purpose, the human-review caveat, and the comparable:false fallback. Missing are explicit parameter mappings and any prerequisites, but the output schema and the overall context reduce the need for return-value detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It does convey that polymarket_id and kalshi_ticker identify the two contracts and that amount_usd relates to 'size-aware' fills, but it does not explicitly explain all four parameters, their accepted values, or the role of 'side'. This leaves significant ambiguity for an agent trying to set correct parameter values.

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 specifies the action (fetch and compare size-aware order-book fills and fees) and the resources (one Polymarket contract and one Kalshi contract). It also distinguishes itself from siblings by adding the unique constraint of only naming a best venue after human review, which is not present in other tools like get_executable_quote or compare_platforms.

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 context on when the tool will and will not produce a best-venue recommendation, explaining the human-review requirement and the fallback for similar titles with different settlement criteria. However, it does not explicitly compare itself to sibling tools or state 'use this instead of X', so it falls 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

A4.4/5.0
Disambiguation4/5

Most tools have clear, distinct purposes, but a few overlap: get_market_odds and get_world_cup_odds both handle World Cup probability questions, and get_edge_signals and get_research_theses both point to potentially mispriced markets. The descriptions help clarify intent, but the boundaries are not always crisp.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case, with verbs like get, list, submit, check, compare, find. Even compound objects like best_price or world_cup_odds fit the pattern cleanly, and no mixed conventions or camelCase appear.

Tool Count5/5

At 14 tools, the server is well-scoped within the 3-15 typical range. Each tool serves a distinct function—odds lookup, market browsing, research, forecasting, and World Cup-specific content—without redundant bloat. The count feels appropriate for the broad domain.

Completeness5/5

The tool set covers a complete workflow: discovering markets, comparing odds, evaluating bets, finding best prices, getting quotes, submitting forecasts, and reviewing personal and AI track records. The lack of an execution tool is intentional (the server is research-oriented), and the append-only forecast model makes missing update/delete operations a non-issue.

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