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Glama

Easypost

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

A4.4/5.0
Behavior4/5

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

Annotations already mark it as read-only and idempotent. The description adds details about data source fan-out (SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail), return format (grouped changes[], total_changes, citation URIs), and parameter specifics. No contradictions 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.

Conciseness4/5

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

The description is somewhat lengthy but well-structured: example queries first, then operation summary, data sources, parameter details, and alternative tool. All sentences add value, though slight trimming could improve conciseness.

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?

Given the tool's complexity (multiple data sources, fallback logic, soft-fail) and no output schema, the description covers behavior comprehensively: return format, parameter formats, source-specific notes, and an alternative tool for static profiles. It leaves no major gaps.

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 covers all parameters (100% coverage), but the description adds value by providing concrete examples for 'since' (ISO date and relative shorthand, with a monitoring suggestion), examples for 'value' (ticker or CIK), and clarifying that 'type' only supports 'company'. This goes beyond the schema.

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 starts with concrete example queries and states 'change feed for a company in the last N days/weeks/months in ONE parallel call', clearly specifying verb, resource, and scope. It lists data sources and explicitly distinguishes from sibling tool entity_profile, earning a top score.

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 advises using entity_profile for static profiles, providing a clear alternative for a different use case. While it doesn't exhaustively list when not to use the tool, the guidance is direct and helpful for an AI agent.

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

A3.7/5.0
Disambiguation2/5

The ask_pipeworx family (stable, beta, grounded) are nearly identical, with beta explicitly matching stable, creating clear misselection risk. The five polymarket_* tools and several research tools (deep_research, bet_research, entity_profile) also overlap in purpose despite detailed descriptions.

Naming Consistency3/5

Tool names are mostly snake_case and readable, with consistent prefixes (ask_pipeworx_, polymarket_, easypost_), but mix verb-first (validate_claim, resolve_entity) and noun-first (entity_profile, ai_visibility_check) conventions. The server name 'Easypost' does not align with the overwhelmingly Pipeworx-focused tool set.

Tool Count2/5

33 tools is a heavy count, especially with three near-duplicate ask_pipeworx variants and many meta-tools. The set is also unfocused: only two shipping tools under an 'Easypost' label while the rest are a broad data-research and prediction-market platform, making the count feel bloated for the apparent scope.

Completeness2/5

As an Easypost server, shipping coverage is severely incomplete (rates and tracking only, no label purchase, address verification, or refunds). Within the Pipeworx tools, the cited pipeworx:// URIs have no direct fetch-by-URI tool, leaving a notable dead end for agents trying to retrieve full records.

Resources