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Glama

Macro Snapshot

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

A4.8/5.0
Behavior5/5

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

Adds valuable context beyond annotations: fans out to multiple sources, fallback, soft-fail, return structure (changes[] grouped by source, total_changes, 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.

Conciseness4/5

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

Front-loaded with examples and purpose. Includes many details, but could be slightly more concise. Still efficient for the complexity.

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?

Complete for a tool with multiple sources, fallback, and varied inputs. Explains return structure and use cases despite no output schema.

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 descriptions cover all parameters; description adds examples for 'since' (ISO date, relative shorthand) and 'value' (ticker or CIK). Adds marginal value over 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?

Description clearly states it provides a change feed for a company, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. Examples like 'What's new with X' and comparison with entity_profile distinguish it from siblings.

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?

Explicitly says when to use (e.g., 'latest on Y', 'news on Tesla') and when not to (use entity_profile for static profile). Includes fallback logic for GDELT→GNews and soft-fail for USPTO.

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

A3.9/5.0
Disambiguation2/5

Several tools form near-overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route questions, and bet_research/polymarket_edges/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread all target prediction-market edges. ask_pipeworx_beta is explicitly identical to ask_pipeworx today, so an agent must read long descriptions to pick correctly. Most other tools are distinct, but the ambiguous clusters pull the score down.

Naming Consistency3/5

All names use snake_case and are readable, but conventions mix: many are verb_noun (ask_pipeworx, compare_entities, discover_tools, subscribe), several are noun phrases (macro_snapshot, indicator, entity_profile, polymarket_arbitrage), and a few are adjective_noun (recent_alerts, deep_research). The near-duplicate ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded suffixes form the only consistent family, but overall the naming pattern is not uniform.

Tool Count2/5

33 tools is well beyond the 15-tool well-scoped range and even past the 25-tool 'too many' threshold. The server tries to be a data router, prediction-market desk, AI visibility checker, memory store, and subscription manager all at once, and includes an intentional duplicate (ask_pipeworx_beta). Several tools (remember/recall/forget, subscribe/unsubscribe/list_subscriptions/recent_alerts) could be their own server.

Completeness4/5

The surface covers question answering, deep research, entity resolution/profile/comparison, macro indicators, prediction-market analytics, subscriptions, memory, and feedback—no obvious dead ends for the main workflows. Minor gaps exist (e.g., no direct generic web search, no update for saved memory other than overwrite, and some niche additions like generate_llms_txt feel out of place), but the core data and research lifecycle is well covered.

Resources