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Google_sheets

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").

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral details: fans out to multiple sources (SEC, GDELT/GNews, USPTO), includes fallback logic (GDELT preferred, GNews when rate-limited), and mentions soft-fail for patents. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph. It front-loads example queries, then explains behavior, parameters, and return structure. Every sentence adds value with no redundancy.

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 tool's complexity (multi-source, fallback, no output schema), the description is complete. It explains input parameters, multi-source fan-out behavior, fallback logic, and return structure (changes[] grouped by source + total_changes + citation URIs).

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 description coverage is 100%, but the description adds further meaning: explains 'since' accepts ISO date or relative shorthand, recommends '30d' or '1m', and clarifies 'value' can be ticker or CIK. This adds context beyond the schema.

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 uses specific verbs ('change feed', 'fans out') and clearly states the tool provides recent updates for a company. It distinguishes from sibling tool entity_profile by explicitly mentioning when to use that instead.

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 clear use case examples ('What's new with X', 'latest on Y') and provides explicit guidance on when not to use this tool ('Use entity_profile instead when you want the static profile'). It also advises on the 'since' parameter.

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

B3.1/5.0
Disambiguation2/5

Several tools are nearly identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same, and ask_pipeworx, ask_pipeworx_grounded, and deep_research all serve overlapping research/query purposes. discover_tools and suggest_questions both act as discovery entry points, while the five polymarket tools differentiate primarily through intricate details that are easy to confuse.

Naming Consistency2/5

Naming conventions are mixed across the set: verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun_verb (sheets_append, sheets_create), noun_noun (polymarket_arbitrage, entity_profile), and bare verbs (forget, recall, subscribe). The sheets tools are internally consistent but the broader collection has no uniform pattern, with awkward names like pipeworx_trending and search_within.

Tool Count2/5

With 36 tools, the server exceeds the 25-tool threshold for 'too many'. The Google Sheets portion accounts for only 5 tools, while the majority are highly specialized Pipeworx and Polymarket tools that could be consolidated or dramatically reduced. The count feels inflated relative to the advertised 'Google_sheets' server name and its actual core purpose.

Completeness2/5

For a server named Google_sheets, the essential operations exist (create, read, write, append, list structure), but there is no delete/clear range tool and no way to add a new sheet to an existing spreadsheet. For the broader data/research scope, important lifecycles are missing (e.g., no direct way to write Pipeworx results into Sheets, no update for subscriptions, only cancel). The set falls short of fully covering either domain.

Resources