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Glama

Tatoeba

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

A4.5/5.0
Behavior4/5

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

Annotations already declare idempotentHint=true, destructiveHint=false, readOnlyHint=false. The description adds valuable context: persistence varies by auth status ('Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours') and key-value storage scoping. It doesn't contradict annotations and discloses behavioral traits beyond them.

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?

Four sentences, front-loaded with the core action, then usage guidance, then persistence details. Every sentence earns its place; no redundant 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?

For a two-parameter, no-output-schema tool, the description covers what it saves, when to use it, storage model, durability, and sibling relationships. It's complete enough for an agent to select and invoke correctly.

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 coverage is 100% with examples for key ('subject_property') and value ('any text'). The description adds no additional parameter semantics beyond the schema, so the baseline of 3 applies.

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 'Save data the agent will need to reuse later', a specific verb+resource statement. It explicitly contrasts with siblings 'recall' and 'forget' by mentioning pairing, and clarifies scope ('scoped by your identifier'), distinguishing it from other 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?

It provides explicit guidance: 'Use when you discover something worth carrying forward...' and gives concrete examples. It also names alternatives: 'Pair with recall to retrieve later, forget to delete.' This satisfies the when/alternatives criteria.

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

B3/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research; multiple Polymarket tools). The memory tools (remember/recall/forget) are generic and could be confused with each other. Overall, there is significant ambiguity in tool selection.

Naming Consistency3/5

Most tool names use snake_case, but there is no strong verb_noun pattern. Some are simple nouns (languages, search) while others are verbs (forget, recall). The naming is readable but not highly consistent.

Tool Count1/5

With 34 tools, the set is far too large for a Tatoeba server. Only 4 tools (search, sentence, translations, languages) are actually related to Tatoeba; the rest cover unrelated domains (Pipeworx data, Polymarket, AI visibility). This is an extreme mismatch.

Completeness2/5

For the Tatoeba domain, the 4 tools cover basic functionality but lack operations like adding or editing sentences. The overwhelming presence of irrelevant tools makes the surface feel incomplete and disjointed for the server's stated purpose.

Resources