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Nihr

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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant behavioral detail: response structure (tracked, expired, snapshot_dates), computation details (decay from daily closes, not intraday), and data availability conditions (snapshots written on cache-miss). No contradictions.

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 verbose but well-organized with clear sections for purpose, response structure, and limits. Every sentence provides useful information, though some streamlining could reduce length without losing 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?

No output schema is provided, but the description fully explains the return format including fields for tracked, expired, and snapshot_dates along with their meanings. It also covers limits (60-day TTL, computation details). For a time-series telemetry tool, this is comprehensive.

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 coverage is 100% (both parameters have descriptions). The description adds minimal extra meaning beyond re-stating defaults and clamping boundaries. Baseline 3 is appropriate as schema already does the heavy lifting.

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 identifies the tool as providing edge persistence and decay telemetry from daily snapshots. It uses specific language like 'answers how long has this edge existed and is it shrinking?' and distinguishes from sibling tool 'polymarket_edges' by focusing on historical time-series rather than current edges.

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 explains when to use this tool—when needing to know edge longevity and decay trends—and contrasts fresh vs. old wide edges. It provides context on limits (60-day TTL) but does not explicitly state when not to use or list alternative tools.

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

The set contains several clusters of near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx/deep_research/validate_claim all handle natural-language queries, and bet_research/polymarket_edges/polymarket_arbitrage scan the same prediction-market space. The descriptions are detailed, but that does not remove the boundary confusion.

Naming Consistency4/5

Almost all tools use lowercase snake_case with recognizable patterns such as verb_noun or prefix_domain (nihr_, polymarket_, pipeworx_). There are minor deviations like ask_pipeworx_beta vs ask_pipeworx_grounded and mixed noun/verb phrasing, but the naming is predictable overall.

Tool Count2/5

36 tools is beyond the typical well-scoped server size, and the set reads as several products bundled together: NIHR grants, Pipeworx data research, prediction markets, memory, subscriptions, and standalone utilities like generate_llms_txt or scan_dependency. Even for a broad data platform this is too many to navigate coherently, and it is a severe mismatch for a server named 'Nihr'.

Completeness3/5

Within its subdomains the set covers core workflows: query (ask/deep_research/validate), entity resolution/profile/comparison, NIHR grant lookup by several dimensions, prediction-market analysis through fill-risk, and memory/subscription lifecycles. But it is a collection of partial products rather than one coherent domain, and some outputs such as pipeworx:// citations or detected arbitrage opportunities lack an obvious in-set tool to consume them further.

Resources