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Glama

Onedrive

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable context: data sources (SEC EDGAR, GDELT/GNews fallback, USPTO), window format, and return structure (changes[] grouped by source, total_changes, citation URIs). 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 fairly long but every sentence adds value. It is front-loaded with usage examples. Minor room for trimming but not wasteful.

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 3 required parameters and no output schema, the description fully covers input semantics, output format, alternative tool, and behavioral traits. Complete for correct invocation.

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?

Schema coverage is 100%, but the description adds significant meaning: explains type is only company, value can be ticker or CIK with example, since accepts ISO date or relative shorthand examples. Exceeds baseline.

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 aggregates changes (SEC filings, news, patents) for a company over a time window. It provides specific examples like "What's new with X" and explicitly distinguishes from sibling tool entity_profile.

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 explicit when-to-use guidance with examples, explains the supported entity type (company only), and contrasts with entity_profile for static profiles. No ambiguity.

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 set mixes near-synonymous routers (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), overlapping discovery tools (discover_tools, suggest_questions), and several prediction-market scanners (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) whose boundaries are easy to blur. The six onedrive_* tools are distinct, but they are buried in an unrelated toolkit where multiple tools appear to address the same task.

Naming Consistency2/5

Naming is split across several conventions: onedrive_*, polymarket_*, and pipeworx_* form consistent clusters, but top-level tools use bare verbs (remember, recall, forget), noun phrases (entity_profile, compare_entities), and varied styles (deep_research, generate_llms_txt, validate_claim). The pattern is readable within clusters, but not predictable across the server.

Tool Count2/5

37 tools is heavy, and the vast majority have nothing to do with the server's name 'Onedrive' — only 6 of 37 target OneDrive, while 31 span Pipeworx data, Polymarket betting, memory, and web utilities. This is a sprawling multi-domain bundle rather than a focused server.

Completeness1/5

As a OneDrive server, the surface is severely incomplete: it offers read-only coverage (list, search, get, profile, shared) but no upload, create, update, move, copy, delete, or share operations, and binary Office/PDF content returns unreadable bytes. The Pipeworx tools are individually comprehensive, but they do not fill the basic lifecycle gaps for the server's apparent file-management domain.

Resources