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Glama

Regrid

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").

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate readOnly and idempotent. The description adds details: fans out to multiple sources, GDELT→GNews fallback, USPTO soft-fails, and output structure (changes[] grouped by source, total_changes count, citation URIs).

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 paragraph of ~100 words, front-loaded with example queries, and every sentence adds meaningful information without 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?

Despite no output schema, the description explains the return format. It covers all key behavioral aspects (sources, fallback, parameter formats) and provides a clear alternative tool reference.

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 coverage is 100%, but the description adds value by explaining the `since` parameter format (ISO date or relative shorthand with examples) and that `value` accepts 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 clearly states the tool returns recent changes (SEC filings, news, patents) for a company in a time window. It includes concrete example queries and explicitly distinguishes from the sibling tool 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?

The description provides explicit guidance on when to use this tool ('What's new' queries) and when not to (use entity_profile for static profile). It also explains the fallback logic for news sources.

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
Disambiguation3/5

Most tools have detailed guidance, but several sets blur together: ask_pipeworx_beta is currently identical to ask_pipeworx, polymarket_edges and polymarket_arbitrage both scan for opportunities, and discover_tools/suggest_questions both serve discovery. The descriptions are strong enough to prevent frequent misselection, but the boundaries are not always crisp.

Naming Consistency3/5

Names are consistently snake_case, but the stylistic pattern is mixed: verb_noun names like generate_llms_txt and list_subscriptions sit alongside bare verbs like remember/forget and noun-phrase names like entity_profile, recent_alerts, and polymarket_arbitrage. Prefixes like polymarket_*, pipeworx_*, and regrid_parcel_* add some order, but the set is not uniform.

Tool Count2/5

33 tools is well above the point where a tool set remains easy to navigate, and several tools are near-duplicates or wrappers: ask_pipeworx_beta duplicates ask_pipeworx, scan_competitor_ai_presence is a wrapper around ai_visibility_check, and the prediction-market scanners overlap. The broad domain explains some of the bulk, but the surface still feels overweight.

Completeness3/5

The server covers a wide range of workflows: lookup, deep research, claim validation, entity profiles, comparisons, subscriptions, memory, prediction-market analysis, and parcel lookup. However, the Regrid parcel side is thin with only address and point lookup, and there is no direct tool for parcel-ID/owner/sales/tax queries. These are real gaps, though the universal router helps agents work around them.

Resources