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Glama

Webflow

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

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: it fans out to multiple sources, describes fallback logic (GDELT→GNews), notes USPTO soft-fails until reactivated, and details the return structure (changes[], total_changes, URIs). Annotations already confirm readOnly and idempotent, so the description enriches understanding.

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, well-structured paragraph. It begins with intuitive example queries, then explains core functionality, sources, parameter details, and return format. Every sentence adds value; no redundancy or fluff.

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?

Given the tool's complexity (multiple sources, fallback, soft-fails) and lack of output schema, the description thoroughly covers return values, source behavior, and alternative tool (entity_profile). It is fully actionable for an AI agent.

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%, but the description enhances parameter understanding: it explains 'since' accepts ISO date or relative shorthand (e.g., '30d'), gives a default recommendation ('30d or 1m'), and clarifies 'value' can be ticker or CIK. This adds practical guidance beyond schema min/max.

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 in the last N days/weeks/months' and lists specific sources (SEC EDGAR, GDELT→GNews, USPTO). It differentiates from the sibling 'entity_profile' by directing users to that tool 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage examples ('What's new with X', 'latest on Y') are provided, and the description advises using 'entity_profile' instead for static profile needs. It also explains fallback behavior (GDELT preferred, GNews when rate-limited). However, it doesn't exhaustively list when not to use this tool.

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

Several tools have blurry boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all target overlapping prediction-market signals, and ai_visibility_check vs scan_competitor_ai_presence plus entity_profile vs recent_changes vs compare_entities partially duplicate each other. The Webflow tools are distinct, but the dominant Pipeworx cluster is hard to navigate.

Naming Consistency2/5

Naming mixes multiple conventions: clean verb_noun for Webflow tools (list_sites, get_collection_item), an ask_pipeworx family, plain single verbs (remember, recall, forget), and long noun-cluster names for prediction markets (polymarket_arbitrage, polymarket_edge_tracker). There is no single predictable pattern across the set.

Tool Count1/5

36 tools is excessive for a server named 'Webflow' — only about six tools actually concern the Webflow CMS (list_sites, get_site, list_collections, list_collection_items, get_collection_item, generate_llms_txt). The remaining ~30 tools form a completely different data-research/prediction-market/memory suite, making the server wildly over-scoped and mislabeled.

Completeness2/5

For the Webflow domain, the surface is read-only: sites and collections can be listed and items fetched, but there are no create, update, delete, or publish operations, leaving obvious lifecycle gaps. The extensive non-Webflow tools do not address the stated server purpose, so the mismatch hurts completeness rather than fixing it.

Resources