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Glama

Anilist

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Description fully discloses the fan-out to multiple APIs, fallback logic, and soft-failure. It aligns with annotations (readOnlyHint, idempotentHint) and adds details about return structure and citation URIs.

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 but well-structured, front-loading purpose and examples. Every sentence contributes necessary information without redundancy.

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?

Despite no output schema, the description specifies the return format (changes[] grouped by source, total_changes count, citation URIs). It covers all parameters, behavior, limitations, and provides sufficient context for an AI agent.

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%, and the description adds value by explaining acceptable formats for 'since' and suggesting typical use ('30d' or '1m'). It clarifies 'value' accepts ticker or CIK, and 'type' is limited to 'company'.

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 returns a change feed for a company, covering SEC filings, news, and patents. It provides concrete use-case examples and distinguishes from the sibling tool 'entity_profile'.

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 tells when to use this tool and when to use entity_profile instead. It explains fallback behavior (GDELT→GNews), parameter format suggestions (e.g., '30d' for monitoring), and notes limitations (patents soft-fail).

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
Disambiguation3/5

Most tools have distinct responsibilities, but several clusters blur together: ask_pipeworx/ask_pipeworx_beta/ask_peworx_grounded are near-identical entry points, the five Polymarket tools overlap heavily, and ai_visibility_check vs scan_competitor_ai_presence overlap in purpose. An agent would need to read long descriptions carefully to avoid misselection.

Naming Consistency4/5

Names are almost entirely snake_case and mostly follow a verb_noun pattern. Minor inconsistency exists in prefixes and verb styles (ask_pipeworx vs pipeworx_feedback vs polymarket_arbitrage vs bet_research), but the naming is generally predictable and readable.

Tool Count2/5

34 tools is well above the 25+ threshold for a cohesive set, and the count is not justified by the server's apparent Anilist scope: the majority of tools are unrelated Pipeworx data-research, prediction-market, memory, and npm-scanning utilities. Many meta-tools could be consolidated.

Completeness2/5

For a server named Anilist, the anime surface is severely incomplete: only search_anime, get_anime, and trending_anime exist, with no seasonal, top-rated, studio, character, staff, or recommendation operations. The Pipeworx data side is fairly complete, but that does not serve the stated domain.

Resources