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Glama

Wayback

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, ensuring safety. The description adds key behavioral details: parallel fan-out to multiple sources, soft-failure for USPTO, and GDELT→GNews fallback on rate limits or 5xx errors. This goes beyond annotations to inform the agent of operational traits.

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 concise yet thorough, covering purpose, sources, fallback, parameter formats, return structure, and sibling differentiation in a well-organized single paragraph. Every sentence adds value, and the structure is front-loaded with key examples.

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?

Since there is no output schema, the description compensates by describing the return format (changes grouped by source, total_changes count, citation URIs). It also covers all key behaviors (parallel calls, fallbacks, soft-fails). For a complex tool with 3 required parameters and no output schema, the description is fully complete.

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 the description adds significant meaning: it explains that 'since' accepts ISO dates or relative shorthand with examples, confirms 'type' is limited to 'company', and specifies that 'value' accepts ticker or CIK. This rich context helps the agent use parameters correctly.

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 that this tool provides a change feed for a company, covering SEC filings, news via GDELT/GNews, and patents. It uses multiple example queries to illustrate common use cases and explicitly differentiates from the sibling 'entity_profile' for static profiles.

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?

The description provides explicit guidance on when to use this tool (dynamic changes over time) versus 'entity_profile' (static profile). It also details fallback behavior between GDELT and GNews, giving the agent clear decision criteria.

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

All Wayback toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in purpose, and the five polymarket_* tools all target prediction-market analysis. The presence of a broad Pipeworx data layer alongside Wayback-specific tools under one server name further blurs boundaries.

Naming Consistency3/5

Most tools use snake_case, but the pattern varies: many follow verb_noun (get_snapshot, list_snapshots, resolve_entity, validate_claim), while others are noun_noun (entity_profile, polymarket_edges, pipeworx_feedback, bet_research). Suffixes like _beta and _grounded are used inconsistently, and some names are long and descriptive while others are terse.

Tool Count2/5

34 tools is far more than what a Wayback Machine server should need, and most tools (Pipeworx data queries, prediction-market analysis, memory, subscriptions) have nothing to do with the Wayback Machine. The server's stated purpose appears narrow, but the tool set is bloated with unrelated functionality.

Completeness2/5

For the Wayback Machine domain, only three tools are relevant (get_capture_count, get_snapshot, list_snapshots), and obvious operations like saving/archiving a URL, comparing snapshots, or handling deleted captures are missing. The Pipeworx-related tools are broad but not clearly aligned with the Wayback theme, so the core purpose is under-served.

Resources