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Glama

Endoflife

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?

Discloses internal behavior: fans out to SEC EDGAR, GDELT→GNews fallback, USPTO; explains fallback logic, PatentsView sunset, and relative date parsing. Annotations already declare read-only and idempotent, and description adds context without contradiction.

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?

Front-loaded with examples and well-structured, though slightly verbose. Every sentence adds value, but could be tightened slightly.

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?

Comprehensive given 3 required parameters and no output schema. Covers sources, fallback, parameter formats, and alternative tool. Leaves no obvious gaps for an agent to use correctly.

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%, but description adds significant detail: explains 'since' accepts ISO dates or relative shorthand (e.g., '7d', '3m'), gives typical values for 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?

The description explicitly states the tool provides a change feed for a company over a time window, with multiple query examples like 'What's new with X'. It clearly distinguishes from the sibling tool entity_profile by advising when to use that instead.

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 this tool (recent changes) versus entity_profile (static profile). Includes example queries and typical intervals like '30d' for monitoring. Clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Endoflife toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route research questions; polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction markets for opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. Long descriptions differentiate them, but an agent selecting quickly could easily pick the wrong one.

Naming Consistency2/5

Naming mixes brand prefixes (ask_pipeworx, pipeworx_feedback), domain prefixes (polymarket_arbitrage), bare verbs (remember, forget, recall, subscribe), and noun-ish phrases (entity_profile, resolve_entity). There is no consistent verb_noun or resource-based pattern across the set.

Tool Count2/5

34 tools is heavy, and for a server named Endoflife only 3 tools actually relate to product lifecycle dates. The rest are unrelated Pipeworx data, Polymarket, memory, and utility tools, making the count feel bloated and mismatched to the apparent purpose.

Completeness3/5

The end-of-life domain itself is reasonably complete: list_products, get_product, and get_cycle cover lookup needs. However, the broader tool surface is a patchwork of unrelated subsystems—data research, prediction markets, memory, AI visibility, subscriptions—with no single cohesive domain that completeness can be judged against.

Resources