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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

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. The description adds rich behavioral details: parallel fan-out to multiple sources (SEC, GDELT/GNews fallback, USPTO with soft-fail), date range handling, and return format (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 well-structured, front-loading the purpose and examples. While slightly verbose, every sentence adds value and there is 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?

The description comprehensively covers the tool's behavior: multiple data sources, fallback mechanisms, soft-failure for patents, date format options, and return structure. No output schema exists, so the description adequately compensates.

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% with descriptions. The description adds practical examples for 'since' (ISO or relative) and specifies that 'value' can be ticker or CIK. This provides additional 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 clearly states the tool provides a change feed for a company over a time window, with specific example queries. It distinguishes itself from sibling entity_profile by noting that entity_profile is for 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 explicit example queries (e.g., 'What's new with X', 'latest on Y') and advises when to use entity_profile instead. It also provides guidance on the 'since' parameter, suggesting '30d' or '1m' for typical monitoring.

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

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but some overlap exists, e.g., ask_pipeworx vs ask_pipeworx_grounded, and several entity/company tools that could be confused. Overall, well-described and mostly disambiguated.

Naming Consistency5/5

Tool names follow consistent patterns: snake_case, with clear prefixes for each group (e.g., polymarket_*, pipeworx_*, get_article, etc.). Naming is predictable and systematic, making it easy to understand the tool's domain and action.

Tool Count2/5

33 tools is high for a server named 'Devto', where only a few tools actually relate to DEV.to. The majority are for Pipeworx data and Polymarket, which are unrelated. The tool count feels bloated and misaligned with the server's apparent primary purpose.

Completeness2/5

For the DEV.to domain, coverage is incomplete: lacks create/update/delete for articles and missing user profile management. The Pipeworx and Polymarket tools are extensive, but that doesn't compensate for the gaps in the core feature set implied by the server name.