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equibles

by io.github.daniel3303

Market-Wide 13F Activity

GetMarketWide13FActivity
Read-only

Get the market-wide 13F leaderboards for a given quarter — which stocks were most bought, most sold, most initiated, or most exited across all 13F filers vs the prior quarter. The bucket argument selects one of: top-buys (Δ shares > 0 ranked by Δ value desc), top-sells (Δ shares < 0 ranked by Δ value asc), new-positions (stocks ranked by count of filers initiating a position), sold-out-positions (stocks ranked by count of filers exiting). Δ Value is the change in published position value: values normally use report-date closing prices, may fall back to filer values, and can be zero when unavailable. It includes price movement on held shares, so use Δ Shares to read the position change itself. The output publishes the first complete 13F report quarter and refuses comparisons that cross that corpus boundary. Use this to answer 'what's the consensus 13F move this quarter?'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bucketYesBucket: top-buys, top-sells, new-positions, or sold-out-positions
maxResultsNoMaximum number of stocks to return (default: 20, clamped to 1-500)
reportDateNoQuarter-end 13F report date in YYYY-MM-DD format, e.g. 2026-03-31 (defaults to the latest available 13F quarter; an off-quarter date snaps to the nearest report on or before it)

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark this as read-only, and the description adds substantial behavioral detail: comparisons are vs the prior quarter, bucket definitions include concrete ranking logic, Δ Value has documented fallback/zero behavior and price-movement caveats, and comparisons across a corpus boundary are refused. This greatly exceeds annotation basics.

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 every sentence earns its place: purpose, bucket definitions, data caveats, boundary behavior, and a concrete use case. The main action is front-loaded, and the advanced details follow logically without padding.

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?

For a market-wide aggregation tool with no output schema, this description covers selection criteria, ranking logic, data nuances, and boundary behavior. The leaderboard semantics plus full schema coverage give an agent enough to invoke the tool confidently and interpret results.

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

Parameters5/5

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

The schema covers parameter names and defaults, but the description adds crucial meaning beyond it: exact ranked semantics for each bucket, how counts are used for new/exited positions, and how to interpret Δ Value vs Δ Shares. This is significantly richer than the schema descriptions alone.

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 verb and resource: retrieving market-wide 13F leaderboards for a given quarter, covering buys, sells, new positions, and exits. Phrases like 'market-wide' and 'across all 13F filers' distinguish this from institution-specific or other market-activity tools.

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?

It gives an explicit use case: answer 'what's the consensus 13F move this quarter?' and explains how the `bucket` argument changes the query. It does not explicitly name sibling alternatives or state when not to use it, but the market-wide vs institution-level distinction makes routing reasonably 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

B3.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions that cross-reference related alternatives. A few near-duplicate names could cause misselection, notably SearchDocument versus SearchDocuments and GetCftcPositioning versus GetLatestCftcPositioning.

Naming Consistency5/5

Tool names consistently follow a VerbNoun camelCase pattern: Get for retrievals, Search for discovery, List/Read for document access, and Add/Close/Remove/Update/Watch/Create/Delete for portfolio mutations. Despite the large count, there is no mixing of naming conventions or unpredictable verb styles.

Tool Count1/5

108 tools is an extreme surface area, far beyond the 3-15 well-scoped range and well past the 25+ threshold. Even for a broad financial data platform, this creates a heavy selection burden and substantial context overhead for agents.

Completeness4/5

The server covers an unusually wide domain: prices, fundamentals, SEC filings, options, insider activity, 13F holdings, short interest, macro data, funds, IPOs, and full portfolio lifecycle management. Notable gaps remain, such as a basic company profile/ticker-resolution tool, dividend history, and analyst estimates, so it is not a perfect 5.

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