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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. Added

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive. The description adds significant behavioral context: multi-source fan-out, GDELT→GNews fallback on rate limits/5xx, USPTO PatentsView sunset soft-fail, and output structure (changes[] grouped by source, total_changes, citation URIs). No contradictions with annotations.

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 dense yet efficient, with every sentence serving a purpose: examples, source behavior, parameter formats, output summary, and cross-reference to entity_profile. It is front-loaded with user query patterns, making it immediately understandable without excess.

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 no output schema, the description specifies the return shape ('changes[] grouped by source + total_changes count + pipeworx:// citation URIs'). It covers fallback sources, edge cases, and the alternative tool, making it complete for a complex multi-source tool.

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 covers all parameters with descriptions, but the description enhances semantics by specifying accepted value formats: `since` accepts ISO dates or relative shorthand ('7d', '30d', '3m', '1y'), `value` is a ticker or zero-padded CIK, and `type` only supports 'company'. This reduces ambiguity 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 identifies the tool as a time-windowed change feed for companies, with concrete natural-language examples ('What's new with X', 'updates on Acme'). It explicitly contrasts with entity_profile, making the tool's scope distinct and differentiating it from sibling tools.

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?

It gives direct usage context via query examples and states when to use an alternative: 'Use entity_profile instead when you want the static profile'. It also describes source fallback behavior (GDELT→GNews) and soft-fail conditions, providing decision-relevant guidance.

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

A3.7/5.0
Disambiguation2/5

The ask_pipeworx family is a major confusion source: ask_pipeworx_beta explicitly states it 'currently matches ask_pipeworx exactly', and ask_pipeworx_grounded/deep_research heavily overlap with the base router. polymarket_edges vs polymarket_arbitrage and discover_tools vs suggest_questions also have fuzzy boundaries, though long descriptions partially mitigate the overlap.

Naming Consistency4/5

All tool names are lowercase snake_case, and most follow a verb_noun pattern (validate_claim, resolve_entity, compare_entities, generate_llms_txt). A few bare verbs (remember, recall, forget) and noun-style names (entity_profile, polymarket_arbitrage, pipeworx_trending) deviate slightly, but the overall style is predictable and readable.

Tool Count2/5

34 tools is heavy and spans several unrelated domains: Pipeworx data research, prediction markets, subscriptions, memory, AI visibility, advice slips, npm dependency checks, and llms.txt generation. The count is inflated by near-duplicate research routers and disconnected outliers like generate_llms_txt and scan_dependency, making the set feel like a kitchen sink rather than a focused server.

Completeness3/5

The Pipeworx research and prediction-market surfaces are quite complete (ask, grounded, deep research, entity profile, compare, resolve, validate, subscriptions with full lifecycle, memory with save/recall/delete). However, the server is named 'advice' yet the advice domain only has three thin tools (get/search/random) with no other operations, and the mixed domains leave obvious dead ends for any single stated purpose.