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Findymail

by io.github.pipeworx-io

Polymarket Fill Risk

polymarket_fill_risk
Read-onlyIdempotent

Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of market (single-market mode) or event (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNoSingle-market: buy_yes | sell_yes | buy_no | sell_no (default buy_yes). Basket: sell_yes | buy_yes (default auto — sell if partition sum > 1, buy if < 1).
eventNoBasket mode: event slug or full polymarket.com URL — checks every leg of the partition.
marketNoSingle-market mode: market slug or full polymarket.com URL.
size_usdNoSingle-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

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 (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are present and consistent. Description adds behavioral details beyond annotations: requires one of market/event, walks ladder, returns verdict and forced_directional_risk, and warns about dominant loss mode. No 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?

Description is lengthy but well-structured with clear sections for single-market and basket modes. Uses bold and uppercase for emphasis. Every sentence adds value, but could be slightly more concise without losing detail.

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?

No output schema, but description fully explains return fields for both modes (top_of_book, vwap, slippage, shares_filled, max_fillable, verdict for single; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg detail, thin_legs, max_clean_notional, forced_directional_risk for basket). Also covers caveats and loss mode. Very 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 description coverage is 100%, but description adds important context: explains size_usd interpretation (spend vs proceeds for single-market; settlement notional for basket), side defaults (auto for basket), and parameter roles. Adds value beyond 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?

Description clearly states 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and explains two modes (single-market and basket) with specific outputs. Distinguishes from siblings like polymarket_arbitrage and polymarket_edges by being a pre-trade risk check.

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 states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Explains why (theoretical overround not capturable, partial fills cause directional risk), providing clear when-to-use and implicit when-not-to-use with alternatives.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research/validate_claim/ask_pipeworx all route factual questions to the same underlying catalog, and ai_visibility_check is essentially wrapped by scan_competitor_ai_presence. The long descriptions help, but the boundaries between query, research, and verification tools are genuinely ambiguous.

Naming Consistency2/5

Names are all lowercase snake_case, but there is no consistent verb_noun or domain pattern: ask_pipeworx, findymail_find_email, scan_competitor_ai_presence, polymarket_kalshi_spread, and generate_llms_txt each use a different structural convention. The mix of brand prefixes, domain prefixes, and bare commands makes the naming feel ad hoc rather than systematic.

Tool Count2/5

33 tools is well over the 25+ threshold and reflects a server that bundles at least five distinct concerns: email lookup, structured data research, prediction-market analysis, subscriptions, and memory. Most individual tools earn their place, but the count is too high for coherent tool selection in a single MCP server.

Completeness3/5

Coverage is broad and mostly self-sufficient for the Pipeworx data ecosystem: querying, deep research, entity resolution, comparisons, verification, subscriptions, memory, and one-off utilities are all present. There are notable gaps though—there is no tool to fetch a full record from a returned pipeworx:// citation URI, and the Findymail side is limited to find/reverse with no verification or bulk capability.

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