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Glama

L2beat

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

A4.5/5.0
Behavior5/5

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

Discloses multi-source fan-out, fallback logic (GDELT→GNews), soft-fail for USPTO, return format (changes grouped, total_changes, URIs), and date format. Annotations already indicate read-only and idempotent; description adds substantial operational detail.

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 somewhat lengthy but front-loaded with purpose. Every sentence adds value; minor redundancy ('in ONE parallel call') but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers multi-source behavior, failure modes, return format without output schema. Adequate for an AI agent to understand invocation. Could mention source-specific limitations briefly but sufficient.

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 description adds meaning by providing examples for 'since' (ISO, relative), acceptable values for 'value' (ticker, CIK), and constraints for 'type'. Adds context beyond schema descriptions.

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 explicitly states what the tool does: a parallelized change feed for a company across multiple sources. It uses multiple example queries and clearly distinguishes from the sibling tool entity_profile, making selection unambiguous.

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?

Provides explicit alternative usage (use entity_profile for static profile) and includes example queries and fallback behavior. Lacks a direct 'when not to use' statement but context is sufficient.

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 tool set mixes L2beat-specific tools with a large number of unrelated Pipeworx, Polymarket, and other domain tools. While individual descriptions are clear, the overall set forces agents to navigate many irrelevant tools, causing confusion about the server's primary purpose.

Naming Consistency2/5

Tool names follow inconsistent conventions: snake_case (list_projects), camelCase (ask_pipeworx), and mixed patterns (generate_llms_txt, bet_research). No unified naming pattern exists across the set, reducing predictability.

Tool Count1/5

With 35 tools, the count is far too high for an L2beat server. Only about 5-6 tools are actually related to L2 scaling; the rest are from other domains, making the tool surface bloated and unfocused.

Completeness2/5

The L2beat-specific tools cover basic operations (list projects, get project, TVS, activity) but lack important features like bridge analysis, project comparisons, or detailed risk breakdowns. The numerous unrelated tools do not address gaps in L2 functionality.

Resources