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Hubspot

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

A4.4/5.0
Behavior5/5

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

The description adds significant behavioral detail beyond the annotations, including time-series construction, signed edge values, trend computation details, expired opportunity handling, snapshot date gaps, and history limits. All annotations (readOnlyHint, idempotentHint, destructiveHint) are consistent and further reinforced.

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 detailed but well-structured: it starts with a clear purpose, then explains arguments, response format, and limitations. It uses formatting (caps for keys) and is front-loaded. Slightly verbose but not excessive given the tool's complexity.

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?

Since there is no output schema, the description takes full responsibility for explaining return values (tracked[], expired[], snapshot_dates[]), their fields, and computational details. It also covers edge cases like gaps and TTL limits, making it complete for an agent to use effectively.

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?

The input schema already has 100% coverage with parameter descriptions. The description adds minor context (e.g., 'snapshot family' for window, max 30 for days) but mostly reiterates schema info. Given the high schema coverage, the added value is marginal.

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 purpose: edge persistence and decay telemetry from daily snapshots. It uses specific verbs (track edges over time, compute first_seen, trend, decay) and distinguishes itself from sibling tools like polymarket_edges (which likely shows current edges only) 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 provides contextual guidance on when to use this tool (e.g., comparing fresh vs. old edges) and implies it complements polymarket_edges. However, it does not explicitly state when not to use it or list alternative tools for different needs.

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 are near-duplicates or overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve overlapping query/discovery purposes. The polymarket tools and HubSpot tools are more distinct, but the set as a whole has fuzzy boundaries between many members.

Naming Consistency2/5

Naming is inconsistent across the set: HubSpot tools use an hs_ prefix, Pipeworx tools mostly use bare verbs (ask_pipeworx, recall, forget), and other tools mix styles (ai_visibility_check, generate_llms_txt, polymarket_edges). The hs_* subset is consistent, but overall there is no single predictable pattern.

Tool Count2/5

38 tools is too many for the apparent scope, especially since the server is named 'Hubspot' but only 6 of the tools are HubSpot-specific. A large portion of the catalog covers unrelated Pipeworx data access, prediction markets, memory, and npm scanning, making the set feel bloated and unfocused.

Completeness2/5

The HubSpot portion of the tool set is read-only: it can get, list, and search companies/contacts/deals, but has no create, update, or delete operations, leaving obvious lifecycle gaps. If the intended domain is actually Pipeworx/data research, the HubSpot tools seem like an unrelated afterthought, so the surface is incomplete for either interpretation.

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