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Glama

Tvmaze

by io.github.pipeworx-io

Remember

remember
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

A4.7/5.0
Behavior4/5

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

Adds context beyond annotations: key-value scoping by identifier, persistence duration (24h for anonymous vs persistent for authenticated). Annotations already declare idempotent and non-destructive, so the bar is lower; description complements well.

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?

Three sentences with clear progression: purpose, usage, technical detail. Front-loaded with the core action. No redundant words; every sentence serves a distinct purpose.

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?

Given no output schema and simple parameters, the description covers all necessary aspects: purpose, usage triggers, storage semantics, and sibling relationships. Complete for the tool's complexity.

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% with descriptions for both parameters. Description adds value with example key patterns (e.g., 'subject_property') and clarifies value as any text, enriching agent understanding beyond 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?

Clearly states the tool saves data for reuse across conversations, with specific verb 'Save' and resource 'data'. Distinguishes from siblings by mentioning 'recall' and 'forget' as paired tools.

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?

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') with concrete examples (ticker, address, preference). Includes scoping and persistence duration, implicitly excluding short-term needs.

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

All Tvmaze toolsInspect
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TDQS

A4.1/5.0
Disambiguation4/5

Tools cover a wide range of domains (TV, finance, betting, memory, etc.), each with detailed descriptions that help distinguish their purposes. However, some overlap exists between related tools like bet_research, polymarket_arbitrage, and polymarket_edges, though the descriptions clarify their distinct uses.

Naming Consistency4/5

Most tool names follow a consistent verb_noun pattern with underscores (e.g., get_show, list_episodes, resolve_entity). A few tools use single imperative verbs (remember, recall, forget), which is a minor deviation but still clear.

Tool Count3/5

The server has 31 tools, which is on the higher side. While each tool has a defined purpose, the scope seems too broad for a server named 'Tvmaze', as many tools are unrelated to TV (e.g., company analysis, betting, memory). A more focused set would improve coherence.

Completeness4/5

For a general-purpose data server, the tool set covers a wide range of tasks: TV show lookups, company research, betting analysis, memory management, and more. The inclusion of ask_pipeworx as a fallback for many queries compensates for missing specific tools. However, the TV-specific functionality is limited to search and schedule, missing details like cast or crew.

Resources