Skip to main content
Glama

Prediction Markets Quant

by com.predictionmarketspicks

Fed Rate Odds (FOMC)

fed_rate_odds
Read-only

Live market-implied odds of a Federal Reserve rate cut, hold or hike at each remaining 2026 FOMC meeting, from Kalshi. Returns the next meeting with days-until and its full strike breakdown, plus the whole remaining rate path and the current fed funds rate. Free, no key. Also returns a cross-venue block comparing Kalshi against Polymarket and CME fed funds futures-implied odds for the next meeting, with the disagreement in percentage points. Free, no key. Use for "will the Fed cut rates", "fed rate hike odds", "next FOMC meeting odds", "what is the market pricing for September", "do Kalshi and Polymarket agree on the Fed".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark it readOnly, and the description adds meaningful behavioral detail: data source (Kalshi), no authentication required ('Free, no key'), and the exact composition of the response (next meeting, strike breakdown, remaining rate path, current fed funds rate, cross-venue comparison with disagreement in percentage points). This goes well beyond the annotations.

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 functional and front-loaded, but it repeats 'Free, no key' twice and uses two separate 'Returns' clauses that could be merged. It remains economical and the query examples at the end add practical value.

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 and zero parameters, the description carries the full burden of explaining the tool's value and response shape. It covers the data source, coverage period, meeting-level details, rate path, current rate, and cross-venue comparison, leaving no critical gap for an agent deciding whether and how to invoke it.

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 tool has zero parameters, so there is nothing for the description to add to the schema. The baseline of 4 applies, and the description appropriately focuses on output composition rather than parameters.

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 states that the tool returns live market-implied odds for Fed rate decisions across remaining FOMC meetings, with a specific focus on Kalshi data. It names the exact resource and distinguishes itself from the unrelated sibling tools through its Fed-specific scope and cross-venue comparison feature.

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 explicit example queries ('will the Fed cut rates', 'fed rate hike odds', etc.) that make it easy for an agent to know when this tool is relevant. It does not explicitly name alternatives or exclusions, but the domain-specificity and example triggers are clear enough context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Prediction Markets Quant toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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