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Arcgis Lancaster

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.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations indicate read-only, open-world, idempotent, non-destructive, so the description adds value by describing internal thresholds (3pp, 20% placeholder fraction), the fill check against live CLOB depth, and the semantic anchor (0.30 Jaccard similarity). It also discloses edge-case behavior like returning null arb signal when placeholder fraction is too high, which is not implied by 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 long but front-loaded with the core purpose and mode selection. All sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) are operational instructions that would otherwise be missing, so the length earns its place. The header-based structure aids scanning and the content is dense with 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?

Despite having no output schema, the description enumerates the response fields (opportunities[] with gap_pp, suggested_trade, etc., and partition_check object) and explains edge-case returns (null arb signal on placeholder fraction >20%, realizable_edge_pp when fill check fails). This makes the tool safe to invoke without external references.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the JSON schema provides basic descriptions for event and topic, the tool description adds concrete examples ('fed-decision-may-2026', 'Strait of Hormuz traffic returns to normal'), explains the outcome of each mode, and clarifies that event walks child markets while topic flattens the union. This enriches the parameter semantics far beyond the schema.

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 the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks, distinguishing it from siblings like polymarket_edges and polymarket_fill_risk. It also names the specific invocation modes (default/event/topic), leaving no ambiguity about what the tool does.

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?

Explicitly instructs when to call with no args (trending_scan), when to use event (specific market) vs topic (cross-event scan), and refers to polymarket_fill_risk for custom sizing. Also provides a negative condition: do not trade when realizable_edge_pp <= 0, giving clear do's and don'ts.

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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve as query/discovery entry points. Polymarket tools also blur together (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk). While many tools have distinct purposes, these overlapping clusters create real misselection risk.

Naming Consistency2/5

All names are snake_case but the pattern is inconsistent: some are verb_noun (ask_pipeworx, search_datasets, validate_claim), some are noun_verb (query_layer, layer_info is noun_noun), and some are single vague words (forget, recall, remember). No consistent verb_prefix or resource_suffix convention, and the mix of meta-tools vs data tools makes the naming feel arbitrary.

Tool Count2/5

34 tools is far too many for a server ostensibly named 'Arcgis Lancaster' — only 3 tools relate to GIS. The bulk is an unrelated general-purpose data/prediction-market toolkit, making the count excessive for the apparent scope. Even as a broad data toolset, 34 tools is on the heavy side and would benefit from splitting into focused servers.

Completeness2/5

The tool surface is a grab bag with no coherent domain, so completeness is hard to assess and clearly lopsided. GIS functionality has search/query/info but no lifecycle management, while the data side has many query/analysis tools but no create/update/delete operations except for subscriptions and memory. Obvious gaps exist for a 'Lancaster' server (e.g., no layer creation, editing, or spatial analysis tools), and the unrelated tools make the set feel incomplete for any single stated purpose.

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