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Glama

Anilist

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds scoping by identifier and persistence differences (authenticated vs. anonymous sessions), which go beyond annotations. No contradiction.

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 four sentences, front-loaded with the core purpose. Every sentence adds value without redundancy. Efficient and well-structured.

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?

The description covers usage, scoping, persistence, and pairing with sibling tools. Given the simple two-parameter schema and no output schema, it provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description's examples for key and value partially overlap with schema examples but do not add significant new meaning. Hence score at baseline.

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 saves data for reuse later, with specific examples of what to store. It distinguishes from sibling tools recall and forget by mentioning pairing, making the purpose unambiguous.

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 explicitly states when to use: when discovering something worth carrying forward. It also advises pairing with recall and forget, providing clear contextual guidance and alternatives.

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

All Anilist toolsInspect
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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