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Glama

Dropbox

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, etc.), the description discloses fan-out to multiple sources, GDELT→GNews fallback logic, USPTO soft-fail limitation, and the output structure. This adds significant behavioral context not available from annotations alone.

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 and front-loaded with example queries, then explains sources, parameters, and output. Every sentence contributes useful information without fluff or redundancy. The length is justified by the tool's multi-source 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?

With no output schema, the description fully covers return values (changes[], total_changes, citation URIs). It also explains the three data sources, fallback behavior, and a known limitation (USPTO sunset). The description is sufficiently complete for a complex, multi-source tool.

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?

Schema coverage is 100%, so baseline is 3. The description adds meaningful semantics beyond schema by providing examples for 'since' (ISO or relative, with recommended '30d'/'1m'), and clarifies 'value' accepts ticker or CIK. This is extra guidance beyond the schema descriptions.

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 it provides a change feed for a company over a recent time window, with explicit example queries. It distinguishes itself from sibling entity_profile by contrasting dynamic changes versus static profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives concrete when-to-use examples (e.g., "What's new with X") and explicitly names an alternative: 'Use entity_profile instead when you want the static profile'. It also explains the fallback behavior between GDELT and GNews, which helps the agent decide if this tool is appropriate.

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

The server name 'Dropbox' leads agents to expect file storage operations, but only 5 of 35 tools are Dropbox-specific. The majority are Pipeworx data lookup and prediction market tools, creating a confusing mismatch between server identity and toolset.

Naming Consistency2/5

Dropbox tools consistently use 'dropbox_verb_noun', but Pipeworx tools mix naming styles: some use 'verb_noun' like 'validate_claim', others use descriptive phrases like 'entity_profile' or 'recent_changes'. The overall set lacks a unified naming convention.

Tool Count2/5

35 tools is high, and the majority are unrelated to the server's name. The Dropbox subset alone is appropriately scoped with 5 tools, but the inclusion of 30 misc data tools makes the set feel bloated and unfocused for a server purportedly dedicated to Dropbox.

Completeness2/5

For the Dropbox domain, the tools cover only basic operations (create folder, download, list, search, metadata) missing update, delete, share. For the Pipeworx domain, the toolset is extensive, but the overall combination lacks completeness for any single coherent purpose.

Resources