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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

A4.7/5.0
Behavior5/5

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

Despite readOnly/annotations already being safe, the description adds substantial behavioral context: snapshots are written on cache-miss (gaps mean no scan), history is bounded by a 60-day TTL, and decay numbers are computed from daily closes of edge_pp_net (not intraday). It also details the response semantics (trend classification, signed edge value, expired opportunities with lifespan), going far beyond the annotations.

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, it follows a clear structure: purpose, the core question, argument details, response format, and limitations. Every sentence adds critical information—no filler. It front-loads the purpose and then logically progresses through inputs, outputs, and edge cases, making it easy to scan.

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 carries the full burden of explaining return values, and it does so thoroughly: tracked[] includes full time-series, first_seen, trend, and decay; expired[] includes lifespan_days; snapshot_dates[] explains data gaps. It also addresses historical limits (60-day TTL) and computational caveats (daily closes vs intraday), making it self-sufficient for an agent to correctly interpret results.

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 covers both params (100% coverage), so the baseline is 3. The description enhances this by explaining the meaning of 'window' as a snapshot family and 'days' as lookback, and it reinforces defaults and max (14/30, default 1wk). This adds context beyond the schema's descriptions, which only state types and defaults.

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 'Edge persistence and decay telemetry' and directly answers 'how long has this edge existed and is it shrinking?', making the tool's core function unmistakable. It actively distinguishes itself from a simple edge snapshot tool by contrasting a 'fresh wide edge' with a '3-week-old wide edge', which differentiates it from sibling tools like polymarket_edges and polymarket_arbitrage.

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 implies when to use this tool: when you need historical edge persistence/decay rather than just current edge values. It gives clear context with the question 'how long has this edge existed and is it shrinking?' and describes the inputs (days, window) and output structure. However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions.

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

C2.9/5.0
Disambiguation2/5

The ORCID-specific tools (record, search, works, work) are reasonably distinct, but the set is dominated by unrelated Pipeworx/Polymarket tools, several of which overlap heavily (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx, creating genuine misselection risk.

Naming Consistency2/5

Naming conventions are mixed with no consistent pattern: nouns like 'record', 'work', 'works', and 'education' sit alongside snake_case verb phrases like 'search_within', 'validate_claim', and 'generate_llms_txt', plus proprietary 'ask_pipeworx' / 'pipeworx_feedback' names. While readable, the styles are inconsistent enough that an agent cannot predict tool names from the domain.

Tool Count2/5

37 tools is far too many for a server scoped as 'Orcid': only roughly 7 tools (record, works, work, education, employment, search, search_within) actually relate to ORCID. The remaining ~30 tools cover prediction markets, npm dependencies, AI visibility, and generic Pipeworx/Polymarket functionality, which is a severe scope mismatch.

Completeness3/5

For read-only ORCID access, the surface covers full records, works, education, employment, and registry search, with semantic search over fetched records as a useful addition. However, it lacks other common ORCID activity types (funding, peer review, distinctions), profile metadata beyond summaries, and any create/update/delete lifecycle operations, leaving notable gaps for a server claiming to serve ORCID data.