Skip to main content
Glama

Jsonschema

by io.github.pipeworx-io

Polymarket Arbitrage

polymarket_arbitrage
Read-onlyIdempotent

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a trending_scan of the top ~200 markets by weekly volume; pass event for the strongest per-event partition_check, or topic for a themed cross-event scan. event (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). topic (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventNoSingle-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
topicNoCross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Polymarket for related events and checks monotonicity across them.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark this as read-only and idempotent, but the description goes far beyond that by disclosing specific behavioral thresholds (3pp partition deviation, 0.30 Jaccard similarity, >20% placeholder fraction) and the fill check rule: 'realizable_edge_pp ≤ 0 means the overround exists only at last-trade; do not trade it.' This adds genuine decision-relevant context.

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 and dense, but every sentence serves a purpose. The use of sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) makes it navigable, and there is no filler content. It is structured well for an agent to extract the required information.

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?

Covers all invocation modes, response structure ('opportunities[]'), filters, edge cases (placeholder rejection, low-similarity pairs), and tradeability warnings. It even points to a sibling tool for custom sizing. With no output schema, the description carries the full burden and meets it completely.

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 schema already provides strong descriptions for both event and topic, so the baseline is high. The description adds behavioral meaning by explaining what each mode does internally (event walks child markets and checks date/threshold ordering; topic searches related events and runs a comparator on the union), going beyond the schema's one-liners.

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 opens with a specific, unambiguous purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' This clearly distinguishes it from siblings like polymarket_fill_risk and polymarket_edges by naming both the resource and the analytical method.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly explains when to use each mode: 'Call with NO args for a trending_scan', 'pass `event` for...', 'or `topic` for...'. It also directs users to polymarket_fill_risk for custom sizing, providing an alternative tool and making the usage context complete.

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

All Jsonschema 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

A3.5/5.0
Disambiguation2/5

Several tools occupy the same entry-point role (ask_pipeworx, ask_pipeworx_beta, deep_research, discover_tools, suggest_questions), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The Polymarket family is carefully described but still has heavily overlapping discovery/edge surfaces. Only validate_json_schema is unambiguous, and it has no related siblings to clarify its position.

Naming Consistency2/5

Tool names mix verb-first forms (discover_tools, generate_llms_txt, resolve_entity), noun-first forms (entity_profile, pipeworx_feedback), and bare verbs (remember, recall, forget). Prefix conventions are inconsistent—ask_pipeworx, pipeworx_feedback, polymarket_edges, recent_changes—and almost none of the names reflect the server name Jsonschema.

Tool Count1/5

32 tools is already too many, but for a server named Jsonschema only one tool belongs to that domain; the rest form a broad data-research and prediction-market suite. This is an extreme scoping mismatch: the set is simultaneously oversized and almost entirely off-purpose.

Completeness1/5

For a JSON Schema server, the surface is essentially one operation: validate_json_schema. Common schema lifecycle operations—generation, parsing, conversion, ref resolution, linting—are missing, leaving most JSON Schema tasks impossible. The unrelated data-research tools are internally rich, but they do not complete the apparent domain.

Resources