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Prediction Markets Quant

by com.predictionmarketspicks

Commodity Edge (Silver / Bitcoin)

commodity_edge
Read-only

Get today's highest-conviction silver or bitcoin trade signal from the PMP edge model — the Kalshi weekly-silver (KXSILVERW) or twice-daily bitcoin (KXBTCD) strike with the largest model edge, as a trade ticket: entry side and price, resolve criterion, model probability, edge in percentage points, confidence tier, and quarter-Kelly sizing. Pro key required. Use for "silver edge today", "bitcoin trade signal", "is there a commodity edge". Pass tickers[] to check specific Kalshi markets — e.g. paste your Kalshi Pro screener watchlist (returns the signal only if it matches the strike PMP is modeling).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickersNoOptional Kalshi ticker watchlist (up to 25) — e.g. paste the tickers from your Kalshi Pro screener or Canvas to get PMP's edge on exactly those markets. Full market or 3-segment event tickers both work. Tickers PMP doesn't model are returned as not_covered (never a fabricated edge).
commodityYesWhich commodity edge to read: silver or bitcoin.

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already declare readOnlyHint and openWorldHint. The description adds critical behavioral context: a 'Pro key required' (authentication need), the output format details, and a guarantee that tickers PMP doesn't model are returned as 'not_covered' (no fabricated edges). No contradictions with annotations.

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 a single, well-structured paragraph that front-loads the core purpose and provides necessary details without redundancy. Every sentence adds value, from the output specification to the usage examples.

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?

Given the tool's complexity (2 parameters, no output schema, annotations covering safety and open-world), the description is fully complete. It explains what the tool returns, prerequisites (Pro key), how to use tickers, and guarantees. No gaps remain for an agent to make an informed decision.

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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the purpose of tickers (paste from Kalshi Pro screener, returns only if matches modeled strike) and clarifying the commodity enum choices. This exceeds the baseline.

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 specifies the exact purpose: retrieving today's highest-conviction silver or bitcoin trade signal from the PMP edge model, detailing the output (trade ticket with entry side, price, resolve criterion, etc.). It clearly distinguishes itself from the sibling tools, which are sports/gambling-focused.

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 provides example queries ('silver edge today', 'bitcoin trade signal', 'is there a commodity edge') and explains when to use the tickers parameter (to check specific Kalshi markets). However, it doesn't explicitly state when not to use this tool or mention alternative tools for similar purposes.

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
Disambiguation3/5

Many tools have clearly distinct domains (fantasy vs NFL vs commodities vs general mispricings), but the 'edge' family is crowded: calculate_ev, scan_mispricings, edge_alerts, find_arbitrage, commodity_edge, nfl_edge, and nfl_prop_edge all surface pricing edges in overlapping ways. Fantasy tools like best_available and who_do_i_draft also have very similar mid-draft recommendation purposes, though their inputs differ.

Naming Consistency4/5

All tool names use lowercase snake_case and are readable, but they mix verb_noun patterns (calculate_ev, compare_players, scan_mispricings) with noun-phrase names (adp_market_gaps, edge_alerts, kelly_size, market_pulse). The style is consistent enough that an agent can predict the convention, with only minor deviations from a strict verb-first pattern.

Tool Count3/5

23 tools is on the heavy side for a single MCP server, though the scope is genuinely broad: prediction-market edge detection, position sizing, probability math, and fantasy football draft tools. It is not bloated enough to feel chaotic, but several tools could be consolidated or are tier-gated variants of the same underlying data.

Completeness4/5

The fantasy football surface covers the draft lifecycle well: rankings, player outlooks, comparisons, ADP gaps, and in-draft recommendations. The prediction-market side covers edge detection, EV, Kelly sizing, base-rate comparison, and arbitrage discovery, though it lacks direct market-price fetching or portfolio tracking—minor gaps that users can work around by supplying prices themselves.

Resources