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Pipedrive

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?

Annotations already cover read-only and non-destructive hints, but the description adds significant behavioral context: snapshot data is written only on cache-miss, gaps reflect days no scan occurred, history is limited by a 60-day TTL, and decay is computed from daily closes (not intraday). This goes well beyond 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?

The description is dense but well-structured, with labeled sections (Args, RESPONSE, LIMITS). Every sentence adds value, especially the detailed response format which compensates for the missing output schema. It is front-loaded with purpose and use case.

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 the lack of an output schema and the tool's complexity, the description is remarkably complete: it explains the response structure, data semantics, and limitations (TTL, snapshot gaps, decay calculation). It leaves no critical gap for an agent to invoke and interpret the tool correctly.

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%, so the baseline is 3. The description adds slight context by calling 'window' a 'snapshot family' and clarifying defaults, but it largely restates the schema. No substantial new parameter meaning is provided.

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 a specific purpose: 'Edge persistence and decay telemetry' and answers a precise question ('how long has this edge existed and is it shrinking?'). It distinguishes from sibling tools by focusing on historical persistence versus likely current-edge tools like polymarket_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 provides clear context for when to use this tool (when edge age/decay matters) and even illustrates the use case ('a 3-week-old wide edge is wide for a reason nobody is willing to take'). However, it does not explicitly name alternatives or state when NOT to use it, which would make it a 5.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, e.g., ask_pipeworx and ask_pipeworx_grounded are nearly identical, and entity_profile, compare_entities, and deep_research all perform multi-source lookups. An agent would struggle to distinguish between them.

Naming Consistency2/5

Naming conventions are highly inconsistent, mixing snake_case (ask_pipeworx), lowercase (deep_research), and prefixed patterns (pipedrive_, polymarket_, pipeworx_). No unified verb_noun pattern exists across the set.

Tool Count2/5

With 35 tools, the count is too high for a server named Pipedrive, which suggests a CRM focus. Many tools are unrelated to CRM (e.g., prediction market, weather, economic data), making the surface feel bloated and unfocused.

Completeness3/5

The Pipedrive subset lacks create/update/delete operations, leaving basic CRUD incomplete. However, the broader data lookup tools cover a wide range of domains (financials, drugs, patents), so overall coverage is moderate but not fully coherent with the server name.

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