tollbooth

cross_platform_arb_scan

Match live Polymarket and Kalshi markets for a topic and compare complementary asks after conservative fee estimates. Intelligence only; verify both rulebooks before acting.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	Topic, entity, asset, or market phrase such as bitcoin
`max_matches`	No	Maximum matches and opportunities returned, default 25
`min_net_edge`	No	Minimum fee-adjusted edge, default 0.015
`min_similarity`	No	Minimum semantic-token match score, default 0.62
`kalshi_max_pages`	No	Kalshi pages of 1000 markets, default 12
`polymarket_limit`	No	Polymarket markets to screen, default 1000

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description conveys it only provides intelligence (no execution), uses conservative fee estimates, and requires verification. It could mention data freshness or rate limits.

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?

Single concise sentence that front-loads the core action and constraints. No wasted words.

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?

Covers purpose and usage well but lacks output structure information. Given no output schema, a brief note on return format (e.g., matched pairs with edges) would improve completeness.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds no extra parameter meaning beyond the topic context.

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 matching live Polymarket and Kalshi markets for a topic, comparing asks with fee estimates, and states 'Intelligence only'. It distinguishes from siblings like polymarket_event_scan and combinatorial_arb.

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?

Explicitly states 'Intelligence only; verify both rulebooks before acting', which informs the agent not to act blindly. However, it does not name alternative tools for specific cases.

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

C2.2/5.0

Disambiguation3/5

Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.

Tool Count1/5

95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.

Completeness2/5

Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.

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