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Glama

randomuser

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
Behavior5/5

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

Annotations already indicate readOnly/openWorld/idempotent behavior, and the description adds substantial context: fan-out to SEC/GDELT/USPTO, fallback triggers, the USPTO soft-fail due to PatentsView sunset, and the return shape. This goes beyond annotation hints and aligns with them.

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 dense but logically organized: query patterns lead off, followed by data sources, parameter formats, return structure, and the alternative tool. Every sentence contributes needed information, and it remains a single focused paragraph.

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 lacking an output schema, the description fully specifies the return structure (changes[], total_changes, citation URIs), covers parameter variations, and addresses edge cases like rate limits and USPTO unavailability. An agent can invoke the tool with confidence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description repeats the same parameter details already present in the schema (ISO/relative formats for since, ticker/CIK for value). No additional parameter-level meaning is introduced, so the baseline of 3 applies.

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's purpose: a change feed for a company over a time window, with query examples like 'What's new with X' and 'updates on Acme.' It explicitly differentiates itself from entity_profile by contrasting dynamic changes versus static profile.

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 explicit guidance on when to use the tool versus alternatives, naming entity_profile as the static-profile alternative. It also explains fallback behavior (GDELT→GNews) and the 'since' window formats, making usage conditions unambiguous.

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
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as identical, and ask_pipeworx_grounded differs only in grounding. generate_users and generate_by_gender overlap, as do ai_visibility_check/scan_competitor_ai_presence and the several polymarket_* tools that all target edge detection and arbitrage. An agent would struggle to select the correct tool.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some start with verbs (generate_users, resolve_entity, validate_claim), some are nouns (entity_profile, deep_research, recent_changes), and some are compound noun phrases (ai_visibility_check, pipeworx_feedback, polymarket_arbitrage). There is no predictable verb_noun structure across the set.

Tool Count2/5

33 tools is well above the 'too many' threshold, and the set includes many meta-tools, memory helpers, and niche prediction-market tools. While the broad domain might justify some diversity, the count feels bloated and dilutes the server's focus, especially given the server name suggests only random user generation.

Completeness4/5

For the apparent core domain (data lookups, research, prediction market analysis, subscriptions), the tool surface is quite comprehensive: it covers direct queries, grounded answers, deep research, entity profiles, comparisons, claim verification, discovery, trends, memory, and subscription management. Minor gaps exist (e.g., no direct CRUD for user-generated profiles beyond creation), but overall the feature set feels well covered.

Resources