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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").

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly and idempotent, but the description adds extensive behavioral details: fans out to multiple sources, fallback mechanisms, soft-fails for USPTO, and return structure with grouped changes and citation URIs. No contradiction.

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?

Front-loaded with example queries and well-structured. Slightly verbose but every sentence adds value. Could be tightened slightly without losing clarity.

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?

Despite lack of output schema, the description fully explains the return structure (changes grouped by source, total_changes count, citation URIs). Covers all essential aspects of this 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 covers all three parameters with descriptions (100% coverage). The description adds extra value by explaining since shorthand (e.g., '30d', '3m'), recommending '30d' or '1m', and clarifying value can be ticker or CIK.

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 like 'change feed', lists example queries, and explicitly distinguishes 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?

Provides when-to-use with example queries, when-not-to-use (use entity_profile for static profile), and fallback logic for news sources and USPTO.

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

Many tools are distinct, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) heavily overlaps—the beta is explicitly identical to the stable version. The two Chile-specific tools are clear, but the presence of numerous unrelated data tools creates confusion about which tool serves the server's purported purpose.

Naming Consistency2/5

Naming is inconsistent: some tools follow verb_noun snake_case (chile_get_tender, chile_search_tenders), others use plain verbs (ask_pipeworx) or noun_verb patterns (polymarket_arbitrage, entity_profile). Mixed conventions and varying levels of specificity make the set feel uncoordinated.

Tool Count2/5

33 tools is excessive for a server named 'Chile Procurement'—only two tools relate to Chile procurement, while the rest are generic Pipeworx data utilities. The count is not scoped to the server's stated purpose; it appears to be a bundled general-purpose toolkit rather than a focused procurement interface.

Completeness2/5

For Chile procurement, only search and get-detail are provided; there is no ability to list all historical tenders, filter by category or amount, or track bidding. The read-only surface covers basic retrieval but lacks common procurement workflows. The broader data tools are complete individually but irrelevant to the server's domain.