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Glama

Rdap

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

A4.6/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and no destructive behavior. Description adds significant context beyond annotations: fans out to multiple data sources, GDELT→GNews fallback, PatentsView soft-fail, and structured return format with citations.

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?

Description is dense but efficient, front-loaded with concrete query examples. Every sentence adds unique value without redundancy. Format is clean and scannable.

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?

For a complex tool with multiple data sources, fallback, and soft-fail, the description is highly complete. It covers what the tool does, what sources it uses, how parameters work, and the return structure. Minor gap: no mention of rate limits or error handling, but overall strong.

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%, so baseline is 3. Description adds: explains 'since' accepts ISO date or relative shorthand, suggests '30d' or '1m' for typical monitoring, clarifies 'value' accepts ticker or CIK, and notes 'type' only supports 'company'.

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 it's a change feed for companies covering SEC EDGAR, GDELT/GNews, and USPTO patents. It includes natural language examples that map to the tool's purpose and distinguishes itself from the sibling 'entity_profile' tool.

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 clear guidance on when to use this tool vs 'entity_profile' (static profile regardless of window). Explains fallback behavior (GDELT→GNews) and soft-fail for PatentsView API. Includes recommended 'since' values like '30d' or '1m'.

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

B3.1/5.0
Disambiguation2/5

The RDAP tools (domain, ip, asn, nameserver, entity) are distinct, but the larger Pipeworx collection creates significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) has heavily overlapping purposes. ai_visibility_check and scan_competitor_ai_presence also overlap.

Naming Consistency3/5

Most tools use snake_case with a descriptive verb_noun pattern (e.g., ask_pipeworx, discover_tools, recent_changes), but the set mixes short single nouns (domain, ip, asn, entity) with compound names, and there are oddities like generate_llms_txt and ask_pipeworx_grounded that deviate from a clear uniform convention.

Tool Count2/5

At 36 tools, the server is far heavier than expected for an RDAP service. The RDAP core only needs ~5 tools; the rest are an unrelated Pipeworx mega-suite (data queries, prediction markets, memory, subscriptions, feedback) that makes the server feel like a dumping ground rather than a focused toolset.

Completeness4/5

For the RDAP portion, coverage is complete: domain, IP, ASN, nameserver, and entity records are all present. The broader Pipeworx features also cover their own workflows (query, research, comparison, memory CRUD, subscription lifecycle), but the server's stated RDAP identity is muddied by these extras, and some integration points (e.g., no direct update for RDAP data, which is read-only anyway) are inherent limitations.

Resources