Skip to main content
Glama

Libretranslate

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?

Adds meaningful behavioral context beyond annotations: key-value storage scoped by identifier, persistence differences between authenticated (persistent) and anonymous (24 hours) sessions. Annotations already cover idempotency and non-destructiveness, but the description doesn't specify overwrite behavior for existing keys, a minor gap.

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 no redundancy. The purpose leads, followed by usage context, storage details, and companion tool references. Every sentence earns its place.

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 simple 2-parameter tool, the description is thorough: covers persistence, scope, examples, and related tools. No output schema is needed for a save operation, and the description fully addresses the tool's role in the agent's workflow.

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 both parameters well-documented. The description reinforces that this is a key-value pair and gives examples, but doesn't add substantial new meaning beyond the 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?

The description clearly states the tool's function: 'Save data the agent will need to reuse later' with concrete examples. It distinguishes itself from siblings by explicitly naming recall and forget as companion tools for retrieval and deletion.

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: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also differentiates from recall and forget by explaining their complementary roles, giving the agent a clear decision framework.

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

All Libretranslate toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying Pipeworx catalog, and the five polymarket_* tools all analyze prediction-market opportunities and edge. While the individual descriptions are detailed, an agent could easily select the wrong tool without deep reading, particularly between ask_pipeworx and its beta/variant versions.

Naming Consistency2/5

The naming style is a mixture of imperative verb phrases (translate, validate_claim, forget, generate_llms_txt), noun phrases (entity_profile, recent_alerts, polymarket_arbitrage), and brand-prefixed nouns (ask_pipeworx, pipeworx_trending, bet_research). While all names are lowercase with underscores, there is no consistent verb_noun or domain-prefix convention across the toolset, making the API feel grab-bag rather than designed.

Tool Count1/5

The server is named 'Libretranslate' — a translation service that needs only translate, detect_language, and list_languages — yet it exposes 34 tools spanning data research, prediction markets, memory storage, subscriptions, dependency scanning, AI-visibility probing, and llms.txt generation. This is an extreme scope mismatch: the overwhelming majority of tools serve completely unrelated functions that have nothing to do with the server's apparent purpose.

Completeness2/5

If judged purely as a translation server, the core surface is present but thin: translate, detect_language, and list_languages cover basic use, though there are no batch, format, or language-details options. If judged as the broader heterogeneous toolset, the domain is incoherent — no single workstream is fully covered, and the unrelated tools (Polymarket betting, Pipeworx research, memory, subscriptions) create a muddled surface with obvious gaps in any single stated purpose.

Resources