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Glama

Quickbooks

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?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds context: makes one parallel call, fans out to specific sources, describes fallback behavior, and explains soft-fail for patents.

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?

A single dense paragraph, well-structured with examples upfront. No redundancy, but could be slightly more concise. Every sentence earns its place.

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?

No output schema, but description fully explains return structure (changes grouped by source, total_changes count, citation URIs). Covers all input parameters and sources.

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 description adds meaning: explains 'since' formats with examples, clarifies 'value' accepts ticker or CIK, and notes only 'company' type supported.

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 in the last N days, fanning out to multiple sources. It specifies the verb (get changes) and resource (company changes), and differentiates from sibling '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?

Explicitly lists query examples ('What's new with X', 'latest on Y') and instructs when to use 'entity_profile' instead. Also explains fallback logic (GDELT→GNews) and parameter usage.

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 (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and there are overlapping clusters among the polymarket_* tools, research tools (deep_research, entity_profile, compare_entities, recent_changes), and AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence). The detailed descriptions help, but an agent selecting among these could easily pick the wrong one.

Naming Consistency2/5

Snake_case is used consistently, but the naming pattern is otherwise mixed: some tools are verb_noun (validate_claim, suggest_questions), some are bare nouns (entity_profile, polymarket_edges), some are verbs without objects (remember, forget, ask_pipeworx), and only the five QuickBooks tools share a qb_ prefix. This creates multiple naming ecosystems with no unified convention.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for 'too many'. More importantly, the server is named Quickbooks but only 5 tools are accounting-related; the other 31 are unrelated Pipeworx data, prediction-market, memory, and meta tools, making the count both excessive and off-purpose.

Completeness2/5

For the QuickBooks domain named by the server, the surface is read-only: get customer, get invoice, list accounts, list invoices, and generic query. There are no create, update, delete, payment, bill, deposit, or report operations, which is a significant gap. For the broader Pipeworx data domain it is fairly complete, but that domain is not what the server name promises.

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