Cross-venue prediction market routing for AI agents with arbitrage, quotes, history, and signals.
Get a deep link URL to place a trade on the best venue for a given market. The URL has a referral code embedded so the platform earns commission. Call this to get the URL, then direct the user to open it.
| Name | Required | Description | Default |
|---|---|---|---|
| side | No | Which side to trade | unknown |
| amount | No | USD amount to trade (for tracking) | |
| source | No | Tool path that produced this execution intent | direct |
| market_id | No | Platform-specific market ID (e.g. "polymarket-abc123") | |
| route_query | No | Original route_market query when source is route | |
| canonical_id | No | Cross-venue canonical market ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses referral code and commission, and that the URL is for user action. However, with no annotations, it misses details like whether the tool validates inputs, what happens on network errors, or if it modifies anything server-side.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load purpose and next step. No redundant or explanatory fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters and no output schema, the description is brief. It doesn't explain how the 'best venue' is determined, the URL format, or error handling. Additional context would improve usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and parameters have descriptions. The description does not add additional meaning beyond the schema, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it gets a deep link URL to place a trade on the best venue for a given market. Differentiates from siblings like get_quote (quote retrieval) and route_market (routing) by focusing on execution via a URL.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tells when to call (to get the URL) and next step (direct user to open it), but lacks explicit when-not-to-use or comparisons with sibling tools. No guidance on prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find live cross-venue arbitrage opportunities — markets where the same event trades at different prices on Polymarket vs Kalshi vs other venues. Can also estimate fee-normalized net spread for a position size.
| Name | Required | Description | Default |
|---|---|---|---|
| venue_a | No | Optional venue filter for one side of the arbitrage pair, e.g. polymarket | |
| venue_b | No | Optional venue filter for the other side of the arbitrage pair, e.g. kalshi | |
| include_fees | No | Whether to include estimated fee/friction normalization | |
| position_size | No | USD position size used for fee/slippage estimation | |
| min_spread_pct | No | Minimum spread in percentage points (default 4 = 4¢) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions live opportunities and fee normalization, but lacks details on data freshness, rate limits, or behavior when no arbitrage exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and contains no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, yet the description does not explain the return format or fields, leaving the agent uncertain about what data to expect from the opportunities.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 general context but does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds live cross-venue arbitrage opportunities and specifies the venues (Polymarket, Kalshi), distinguishing it from sibling tools like get_quote or get_history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for identifying arbitrage and optionally estimating fee-normalized spread, but does not explicitly state when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get historical OHLCV-style prediction market candles over a time range, including the latest stored depth snapshot within each bucket when available. Can also export CSV text or base64-encoded Parquet.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Inclusive ISO 8601 end timestamp | |
| from | Yes | Inclusive ISO 8601 start timestamp | |
| venue | No | Venue filter, e.g. polymarket or kalshi | |
| format | No | Return JSON candles, CSV text payload, or base64-encoded Parquet payload | json |
| market_id | No | Platform-specific market ID | |
| granularity | No | Bucket size for history candles | 1d |
| canonical_id | No | Cross-venue canonical market ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the inclusion of depth snapshots and multiple output formats, but omits behavioral traits like error handling, rate limits, ordering, pagination, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the core action and key features. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description covers the main purpose, it lacks details on output structure (e.g., JSON candle fields) since there is no output schema. For 7 parameters and no output schema, more context on return format would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. The description adds value beyond schema by explaining the nature of the data (OHLCV-style candles, depth snapshot, export formats) which is not in individual parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies exactly what the tool does: get historical OHLCV-style prediction market candles over a time range, including depth snapshots and export options. It clearly distinguishes from siblings like get_quote (real-time) and get_arbitrage (relative pricing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for historical data retrieval but does not explicitly state when to use it versus alternatives, nor does it provide exclusion criteria or prerequisites. No guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the live probability, liquidity, and volume for a prediction market across all venues. Can also return venue order book depth and size-aware VWAP/slippage estimates.
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | USD size for YES-buy VWAP/slippage estimation | |
| market_id | No | Platform-specific market ID | |
| canonical_id | No | Cross-venue canonical market ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses the data returned (live metrics, optionally depth/VWAP) but does not mention any behavioral traits like read-only nature, rate limits, or authentication needs. This is insufficient for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 25 words, front-loaded with the core purpose. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main functionality but omits details like parameter relationships (market_id vs canonical_id), return format, and error conditions. Without an output schema, more context would help, but the description is minimally adequate for a 3-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 new semantic info; the mention of VWAP/slippage when 'size' is provided is already stated in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves live probability, liquidity, and volume for a prediction market across all venues, with optional depth and slippage estimates. This distinctively separates it from siblings like get_arbitrage, get_history, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for live market data but lacks explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, though sibling tools differ in function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find questions that users are searching for but no prediction market exists. Useful for discovering new market creation opportunities or identifying emerging topics with real trading interest.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 7) | |
| limit | No | Max results (default 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It does not disclose data freshness, pagination, response format, or any constraints beyond what the schema implies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the core purpose and immediately follow with usage scenarios. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low parameter count, full schema coverage, and no output schema, the description provides adequate context for basic invocation. Lacks details on result format but remains sufficient for initial understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters (days, limit) have descriptions. The tool description adds no additional context beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Find questions that users are searching for but no prediction market exists' with a specific verb and resource. This distinguishes it from sibling tools like execute_url, get_arbitrage, etc., which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description mentions it is 'useful for discovering new market creation opportunities or identifying emerging topics' which suggests when to use, but does not explicitly exclude use cases or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find the best prediction market for a natural-language question or trading intent. Returns the best cross-venue match, current probability, liquidity, and any arbitrage opportunity between venues.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Natural language question or trading intent, e.g. "Will BTC reach $200k in 2025?" or "Fed rate cut September" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It describes the return values but does not mention side effects, authentication, or rate limits. However, the tool appears read-only with no destructive hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action, no redundant words. Efficiently conveys purpose and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with no output schema, the description sufficiently explains what is returned and how to invoke it. Sibling tools cover other aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (parameter q described). The description adds helpful examples (e.g., 'Will BTC reach $200k in 2025?') that enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds the best prediction market for a natural-language question, with specific outputs (match, probability, liquidity, arbitrage). It distinguishes from siblings like get_arbitrage and get_quote.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use it (for natural-language questions/trading intents) but does not provide when-not-to-use or explicit alternatives. However, the context of sibling tools implies differentiation.
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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Machine-payable prediction-market briefs and probability-change monitoring for AI agents.
Calibrated probabilistic foresight for AI agents, powered by live prediction-market signal.
Prediction-market intelligence for conservative cross-market pricing edges and live signals.
The Brain Layer for AI Trading Agents — quant calls + cross-venue arb across perp venues via MCP.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: deep link generation, arbitrage detection, historical data, live quotes, unmet demand discovery, and market routing. No two tools overlap in functionality.
All tool names follow a consistent verb_noun pattern with snake_case (execute_url, get_arbitrage, get_history, get_quote, get_unmet_demand, route_market), making them predictable and easy to differentiate.
Six tools is well-scoped for a prediction market surveillance and trading facilitation server. Each tool covers a distinct aspect of the domain without unnecessary bloat or insufficiency.
The tool set covers the full lifecycle from market discovery (route_market, get_arbitrage, get_unmet_demand) to data analysis (get_history, get_quote) to action (execute_url), with no obvious gaps given the server's stated purpose.
