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Tiingo

by io.github.pipeworx-io

Polymarket Edge Tracker

polymarket_edge_tracker
Read-onlyIdempotent

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback in days (default 14, clamp 2-30).
windowNoWhich polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

Schema Changelog

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

  1. Added
  2. Removed
  3. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description provides rich behavioral details: snapshot TTL limits, the meaning of gaps in snapshot_dates, the fact that decay is computed from daily closes rather than intraday, and the distinction between tracked and expired opportunities. This significantly exceeds the annotation baseline.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although the description is long, every sentence serves a purpose: explaining the core question, response structure, and limitations. It is front-loaded with the primary purpose and uses caps and line breaks for readability. No fluff or 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?

Given there is no output schema, the description thoroughly explains the response fields (tracked[], expired[], snapshot_dates[]) and their semantics, along with data limitations. This gives the agent enough context to interpret results correctly and set expectations.

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

Parameters3/5

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

Schema description coverage is 100%, and both parameters (days, window) are already documented with defaults and allowed values. The description repeats these details and adds minor nuance (e.g., 'snapshot family'), but does not substantially increase understanding 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's specific purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a distinct question ('how long has this edge existed and is it shrinking?') and differentiates from the sibling polymarket_edges by emphasizing historical snapshot analysis rather than current edge extraction.

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 gives strong context for when to use the tool by framing it around persistence and decay questions, noting that a fresh wide edge and a 3-week-old wide edge are different trades. It does not explicitly state when not to use it or name alternative tools, but the differentiation from polymarket_edges is implicit and clear.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ only in mode; several Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, bet_research) target related opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. The meta-tools (discover_tools, suggest_questions, pipeworx_trending) could also be confused for one another.

Naming Consistency3/5

All names are lowercase snake_case, which is consistent, but patterns vary: some are verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun (news, crypto_prices, stock_metadata), and several use brand prefixes (pipeworx_*, polymarket_*). This mixed convention is readable but not predictable.

Tool Count2/5

35 tools is too many for a coherent, well-scoped server. The set bundles a financial data API (Tiingo) with a generic data router (ask_pipeworx), prediction-market tools, memory utilities, subscription management, and npm checks — many unrelated to the server's apparent purpose, making it feel bloated.

Completeness2/5

For a Tiingo server, core data coverage is limited to stock prices, stock metadata, crypto prices, and news — missing real-time quotes, fundamentals, forex, technical indicators, and other typical Tiingo endpoints. Conversely, the general Pipeworx platform has broad query/research/subscription coverage but that domain doesn't align with the server name, leaving significant functional gaps.

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