Skip to main content
Glama

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

A4.7/5.0
Behavior5/5

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

Despite annotations already indicating read-only/idempotent safety, the description adds rich behavioral detail: fan-out to SEC/GDELT/USPTO, fallback and soft-fail behavior, return structure with changes[] grouped by source, and citation URIs. No contradiction 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?

Dense but highly efficient; every sentence delivers value: query examples, source fan-out, fallback logic, parameter format, return structure, and alternative tool. Front-loaded with common use cases.

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?

With no output schema, the description compensates by describing the return shape (changes[] grouped by source, total_changes, citation URIs). It also covers source-specific behavior, window semantics, and the key alternative tool, making the tool's operation fully understandable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all three parameters fully (100% coverage), so baseline is 3. The description reinforces the `since` shorthand and ticker/CIK formats but does not add substantive meaning beyond the schema; it contextualizes rather than extends parameter semantics.

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's function: a change feed for a company over a time window, fanning out to multiple sources. It distinguishes itself from sibling entity_profile by specifying static profile vs. change feed.

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 explicit natural-language query examples and an explicit 'instead' alternative: 'Use entity_profile instead when you want the static profile'. This gives clear when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all analyze prediction-market opportunities; discover_tools and suggest_questions both serve as meta-tool onboarding. An agent could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but naming patterns are mixed: some are verb_noun (get_flood_forecast, list_subscriptions), some are noun-centric (entity_profile, bet_research), some are bare verbs (remember, recall, forget), and some use long descriptive phrases (ask_pipeworx_grounded, polymarket_kalshi_spread). It is readable but lacks a single predictable convention.

Tool Count2/5

33 tools is heavy for a server whose apparent core is flood forecasting — only 2 of 33 tools (get_flood_forecast, get_river_discharge) relate to flooding. The bulk is a sprawling Pipeworx data-access, prediction-market, and memory layer, making the surface feel overstuffed and off-topic relative to the server name.

Completeness2/5

If the intended domain is flood data, the surface is severely incomplete: only forecast and discharge lookups exist, with no historical flood events, alert subscriptions, mapping, or severity-warning tools. If instead the domain is meant to be Pipeworx-style data research, the surface is broad but still has gaps (no direct SEC filing text retrieval, no clear update/delete lifecycle for many resources). Either way, the purpose is unclear and coverage is mismatched.