Skip to main content
Glama

Jsonplaceholder

by io.github.pipeworx-io

Polymarket Edges

polymarket_edges
Read-onlyIdempotent

Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoTop N edges to return after ranking. Default 10, max 25.
windowNoPolymarket volume window to filter markets. Default 1wk.
min_kellyNoMinimum half-Kelly fraction (as decimal, e.g. 0.005 = 0.5% of bankroll) to include single-leg opportunities. Default 0 (no filter). Skips opportunities that are too small to bet sensibly even if the edge is large.
min_edge_ppNoMinimum |edge| in percentage points to include (default 0.5). Edge is evaluated NET of slippage.
slippage_ppNoAssumed execution slippage in percentage points per leg (default 0.3). Subtracted from raw |edge| before ranking and Kelly sizing. Polymarket has zero trading fees as of 2024 but bid/ask + thin depth typically eats 20-50bp per trade. Bump for very thin partitions; drop to 0 if you have a smarter fill model.
max_spread_ppNoTradeable-edge filter. Maximum bid/ask spread in percentage points on the representative market. Default null (no filter). Set to 2 to require tight books — anything wider eats most plausible edges.
min_liquidityNoTradeable-edge filter. Minimum $ liquidity on the representative market (or for partition_overround, on at least one top_leg). Default 0 (no filter). Set to 5000 to drop thin-book opportunities where executing the edge would walk the book past breakeven.
category_filterNoComma-separated list to restrict the output: "model_driven" (crypto_price + news_momentum), "structural_arbitrage" (partition_overround), "concentrated_longshot". Combine like "model_driven,structural_arbitrage". Default: all.
min_partition_leg_kellyNoMinimum BEST per-leg half-Kelly fraction across a partition_overround opportunity's top_legs (or longshot_basket legs). Default 0 (no filter). Partition arbs always return kelly_fraction_half=0 at the parent level by design (basket trades don't compose to single-leg Kelly), so min_kelly never filters them — this knob applies to the per-leg Kelly inside top_legs instead. Use to suppress thin partitions whose individual leg edges aren't worth the per-leg slippage cost.

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnly=true and destructive=false, but the description goes far beyond by disclosing the model families (crypto_price from 90d FRED log-returns, news_momentum from GDELT), the response structure (by_segment, fed_candidates, _diagnostics), caching behavior ('Cached 1h at the KV level keyed on all knobs'), and caveats like the 24h-move warning and slippage adjustments. This rich behavioral context enables the agent to understand expected outputs and limitations without contradiction.

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 long but well-structured with explicit labels (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT, TRADEABLE-EDGE KNOBS, RESPONSE TOP-LEVEL) and front-loads the core purpose. Every section conveys necessary detail for a complex tool, so while not terse, it earns its length. A 5 would require even tighter wording with no redundancy.

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, the description must explain the return shape, and it does: it details the top-level structure (by_segment, fed_candidates/fed_note, _diagnostics), per-opportunity fields (edge_pp_net, kelly_fraction, market.liquidity, spread_pp, volume, 24h-move warning), and why segments may be empty via funnel counters. It also covers caching and the rare-by-design nature of one segment. For a tool with 9 parameters and complex output, this is complete.

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 coverage is 100%, so the baseline is 3, but the description adds meaningful context for key knobs: it explains that min_liquidity/max_spread_pp 'drop opportunities where edge isn't realizable' and clarifies min_partition_leg_kelly's interaction with per-leg Kelly in partitions. It also adds real-world slippage context ('Polymarket has zero trading fees as of 2024 but bid/ask + thin depth typically eats 20-50bp per trade'). This exceeds schema-only semantics without covering every parameter in prose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource+scope: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It clearly states the tool's purpose and target use case. However, it does not explicitly differentiate from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, though the detailed segments (model_driven, structural_arbitrage, concentrated_longshot) imply a broader scope.

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 clear usage context: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It also explains when to adjust knobs (e.g., 'Bump for very thin partitions; drop to 0 if you have a smarter fill model') and notes that Fed bets are excluded from ranking. Yet it lacks explicit when-not-to-use or alternative tool references, 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.

All Jsonplaceholder 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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all serve natural-language question answering. The JSONPlaceholder get_* tools are distinct but introduce an unrelated domain, making tool selection ambiguous in practice.

Naming Consistency3/5

All names use lowercase snake_case, which is a consistent style, but there's no uniform verb_noun pattern. Tools mix action-first names (get_posts, remember, resolve_entity) with noun-oriented names (entity_profile, deep_research, pipeworx_trending). The naming is readable but not highly predictable.

Tool Count2/5

35 tools is far beyond the typical well-scoped range of 3-15. The set includes a full suite of Pipeworx/Polymarket tools plus a separate JSONPlaceholder demo namespace, making the server feel bloated and unfocused. Many meta-tools (discover_tools, suggest_questions, pipeworx_trending) could be consolidated.

Completeness2/5

The server's name and the get_posts/get_post/get_comments/get_users tools suggest a JSONPlaceholder fake API, but CRUD operations are missing: there's no create, update, or delete for posts, comments, or users, and no todos, albums, or photos. The unrelated Pipeworx/Polymarket tools don't address this core domain gap.

Resources