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Glama

Futuur

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?

Beyond the read-only, idempotent, non-destructive annotations, the description reveals multi-source fan-out (SEC EDGAR, GDELT→GNews, USPTO), PatentsView sunset soft-fail, and the structured return shape (changes[], total_changes, pipeworx:// URIs). This is rich behavioral context the annotations alone do not provide.

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 dense yet front-loaded with intent phrases. Every sentence contributes essential information: query patterns, source behavior, time formats, fallback logic, return structure, and an explicit sibling alternative. There is no filler.

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?

With no output schema, the description compensates by stating the exact return structure (structured changes[] grouped by source + total_changes + citation URIs). It also covers operational caveats (rate limits, API sunset, soft-fail) and provides tool differentiation, making it comprehensive for a multi-source tool.

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%, so a baseline of 3 applies. The description adds meaningful guidance for `since` with formats and examples, plus a best-practice recommendation ('Use "30d" or "1m" for typical monitoring'). It does not add new semantics for `type` or `value`, but the added usage nuance justifies a 4.

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 opens with concrete user intents ('What's new with X', 'latest on Y') and immediately identifies the tool as a change feed for a company over a time window. It explicitly distinguishes itself from entity_profile, which serves the static profile, making its 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit usage examples, explains source fallback behavior (GDELT preferred, GNews on rate limits/5xx), and names an alternative tool ('Use entity_profile instead when you want the static profile...'). This clearly tells the agent when to use this tool versus a sibling.

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

Multiple tools have overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions with varying degrees of grounding. The polymarket_* family (edges, arbitrage, fill_risk, edge_tracker) and bet_research also create boundary confusion. An agent would struggle to consistently select the correct tool without deep reading of each description.

Naming Consistency3/5

Most tools follow a verb_noun snake_case pattern (search_markets, get_market, compare_entities, resolve_entity), but there are clear outliers like pipeworx_trending, recent_alerts, top_markets, and pipeworx_feedback which use adjective_noun or noun_adjective forms. The pattern is mostly consistent but has enough deviations to feel mixed.

Tool Count2/5

With 34 tools, the surface is decidedly heavy. Many tools serve entirely different domains (memory, subscriptions, AI visibility, npm dependency scanning, cross-venue arbitrage) rather than a unified purpose. This feels like several server concepts merged into one, making the count inappropriate for a single coherent server.

Completeness3/5

The data-research side is fairly comprehensive: querying, grounding, comparisons, profiles, claim validation, entity resolution, change tracking, and memory are all covered. However, for the nominal Futuur prediction-market domain, only read-only market lookup exists — no trading, account management, or order placement. Subscription CRUD is also missing an update operation, and several research tools only cover US public companies and specific data sources.

Resources