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Glama

Prh Fi

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

A4.3/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, non-destructive. Description adds details: fans out to SEC, GDELT (with fallback to GNews), USPTO soft-fail, and returns grouped changes with citation URIs. Behavior is well-described beyond 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 dense but well-structured, starting with examples and then detailing behavior. It is not overly verbose, though a bullet list for sources might improve scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple sources, fallback, relative dates) and no output schema, the description covers key aspects: window parameter, data sources, return structure (grouped changes, count, URIs), and a sibling comparison. A minor gap is the lack of detail on result quantity or pagination.

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 100% of parameters. Description adds value by clarifying type enum (only company), giving examples for 'since' (ISO date, relative shorthand, typical '30d'), and explaining 'value' as ticker or CIK. These enrich the schema definitions.

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 the tool provides a change feed for a company over a time window, covering SEC filings, news, and patents. It cites example queries and distinguishes from 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 Guidelines4/5

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

The description explicitly recommends entity_profile for static profiles, implying recent_changes is for dynamic updates. It provides query examples but does not exhaustively list when not to use.

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

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and compare_entities all retrieve factual data about entities. Additionally, multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) are highly specialized but can be confused without careful reading of descriptions.

Naming Consistency1/5

Naming conventions are highly inconsistent: snake_case (ai_visibility_check, generate_llms_txt), lowercase phrases (ask_pipeworx, forget, recall), and mixed styles (entity_profile, scan_competitor_ai_presence). No clear pattern like verb_noun; tools like 'remember' and 'forget' are single words while others are lengthy phrases.

Tool Count3/5

32 tools is on the high side for a single server, but the coverage is broad (data retrieval, prediction markets, Finnish registry, memory, utilities). However, many tools are redundant or overly specialized, making the count feel bloated. A more focused set could be 20-25 tools.

Completeness3/5

The Pipeworx tools provide deep coverage for factual data retrieval, and the Finnish registry has basic operations. However, there are gaps in other areas, and the set lacks a unified domain. The inclusion of memory tools and a random llms.txt generator feels out of place, breaking the coherence.

Resources