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Glama

Ripe Stat

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds significant behavioral context: fan-out to three sources (SEC EDGAR, GDELT→GNews fallback, USPTO), handling of rate limits/5xx errors, USPTO sunset info, and return structure (changes grouped by source + citation 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.

Conciseness5/5

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

The description is compact yet comprehensive, with every sentence providing unique value. It front-loads usage examples and clearly separates purpose, sources, parameters, and alternatives. No redundant or filler content.

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?

For a tool with 3 parameters (all described), no output schema, and rich annotations, the description fully covers the tool's domain: data sources, fallback logic, return structure, and relationship to sibling tool. An agent can confidently invoke it.

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 value beyond: clarifies 'since' accepts ISO dates or relative shorthand (7d, 30d, 3m, 1y), suggests '30d' for monitoring, and notes that 'type' currently only supports 'company'. This enhances usability.

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 provides specific verb-resource ('change feed for a company') and includes example queries like 'What's new with X' and 'updates on Acme'. It explicitly distinguishes from the sibling tool entity_profile, making the purpose unmistakable.

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?

The description clearly states when to use this tool vs. entity_profile ('Use entity_profile instead when you want the static profile'). It also provides guidance on the 'since' parameter with suggested defaults ('Use 30d or 1m for typical monitoring'), though it lacks explicit exclusions for non-entity queries.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially in networking and domain-specific queries. However, the multiple `ask_pipeworx` variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and `deep_research` have overlapping functionality, which could cause occasional misselection.

Naming Consistency2/5

Tool names use inconsistent conventions: some are concatenated (geoloc, whois), some use underscores (abuse_contact, as_overview), and some are multi-word (scan_competitor_ai_presence, ask_pipeworx_grounded). No consistent pattern across the set.

Tool Count3/5

39 tools is high for a single server, but justified by the broad scope covering networking, finance, prediction markets, AI visibility, and more. However, many tools are meta-tools for the Pipeworx ecosystem, making the set feel slightly bloated.

Completeness5/5

The server covers an exceptionally wide range of domains comprehensively, with tools for networking, company data, FDA datasets, prediction markets, weather, news, and more. It includes memory and subscription management, leaving few obvious gaps for its stated data-retrieval purpose.

Resources