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Glama

Urlscan Io

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

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), description details multi-source fan-out, GDELT→GNews fallback, PatentsView soft-fail, and return structure (changes[], total_changes, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is slightly long but front-loaded with examples and covers many aspects efficiently. Minor room for improved structure without losing information.

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 no output schema, the description covers use cases, parameter behavior, source interactions, and edge cases (PatentsView soft-fail, fallback) comprehensively.

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%, and description adds meaningful context: explains since format (ISO/relative) with recommendation, restricts type to 'company', and describes value as 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?

Description clearly defines the tool as a 'change feed for a company in the last N days/weeks/months' with example queries. It distinguishes from sibling 'entity_profile' by specifying when to use each.

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 example queries to guide usage, explicitly states when to use entity_profile instead (for static profiles), and notes fallback sources and limitations like PatentsView API sunset.

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

B3.3/5.0
Disambiguation3/5

Many tools have distinct purposes with thorough descriptions, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route questions to data sources. The multiple polymarket tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread) also require careful reading to differentiate. The urlscan tools (domain, ip, search, submit, result) are distinct, but the overall set mixes several unrelated domains, increasing misselection risk.

Naming Consistency2/5

Naming is a mix of conventions: short urlscan verbs (domain, ip, search, submit), noun-first names (entity_profile, recent_changes, deep_research), verb_noun names (compare_entities, resolve_entity, generate_llms_txt), and prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun pattern or consistent prefix convention across the set. This inconsistency makes it hard to predict what a tool does from its name alone.

Tool Count2/5

With 36 tools, the set is far above the 3-15 range typical for a coherent server, even for a broad data API. The inclusion of meta-tools like discover_tools and suggest_questions suggests the count is so high that agents need help navigating it. The load is compounded by tools spanning urlscan.io, Pipeworx, prediction markets, memory, subscriptions, and feedback, making the server feel like a grab bag rather than a focused service.

Completeness3/5

The urlscan portion is complete for searching, submitting, and retrieving scan results, and the Pipeworx side covers a wide range of data and analysis capabilities. However, the server is named 'Urlscan Io' while most tools are unrelated to urlscan, creating a mismatch between the stated purpose and the actual surface. There are no obvious gaps for the included features, but the lack of a coherent domain makes it hard to assess what 'complete' means for this set.

Resources