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Glama

Urlhaus

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. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true. Description adds valuable context: fans out to multiple sources, explains fallback and soft-fail mechanisms, and describes return structure. 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?

Front-loaded with example queries. Every sentence serves a distinct purpose: purpose, sources, parameter formats, return structure, alternative. No wasted words, well-structured for quick understanding.

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, description fully explains return structure (changes[] grouped by source, total_changes count, citation URIs). Covers source behaviors, parameter formats, and edge cases like USPTO soft-fail. Complete for an agent to use correctly.

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 description coverage is 100%. Description adds extra meaning beyond schema: explains 'since' format with relative shorthand examples and recommends '30d' or '1m', clarifies 'value' can be ticker or CIK, and reiterates 'type' limitation.

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' from multiple sources, with specific verbs like 'fans out'. It distinguishes itself from sibling 'entity_profile' and lists example queries that match user intents.

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?

Explicitly provides when-to-use scenarios with example queries. Gives a direct alternative: 'Use entity_profile instead when you want the static profile'. Also explains source behavior (GDELT→GNews fallback, USPTO soft-fail) guiding appropriate use.

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

A3.6/5.0
Disambiguation2/5

The server contains three ask_pipeworx variants, with ask_pipeworx_beta explicitly documented as 'currently matches ask_pipeworx exactly' — two functionally identical tools right now — and ask_pipeworx_grounded differing only in extraction mode. Five polymarket_* tools also occupy overlapping territory (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread), and meta-tools like discover_tools, suggest_questions, and pipeworx_trending blur together. Only the four URLhaus lookup tools are cleanly distinct.

Naming Consistency3/5

There is good internal consistency within subgroups (lookup_host/payload/url, ask_pipeworx*, polymarket_*, remember/recall/forget), but across the full set conventions are mixed: verb-driven names (get_recent, list_subscriptions, validate_claim, generate_llms_txt) sit alongside plain noun names (entity_profile, bet_research, deep_research, recent_changes). The pattern is readable but lacks a single unifying scheme.

Tool Count2/5

At 35 tools this is excessive for any single scope, but the deeper problem is that the server is named Urlhaus while roughly 30 of its 35 tools are unrelated Pipeworx/Polymarket infrastructure. The actual malware-mapping surface is only 4 tools; the rest is bolted-on and inflates the count well past the 25+ threshold for a heavy set.

Completeness2/5

For the server's intended URLhaus domain, the surface is read-only lookups (recent, host, payload, url) with no submission, payload-download, or tag-management operations, leaving obvious lifecycle gaps. Meanwhile the Pipeworx portion is all meta-framing around a single ask_pipeworx router, so neither domain is deeply or fully covered — the set is wide but shallow across many unrelated areas.

Resources