Skip to main content
Glama

Tatoeba

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Discloses fan-out to SEC EDGAR, GDELT→GNews fallback (with rate-limit/5xx conditions), and USPTO soft-fail due to PatentsView sunset. This goes beyond the readOnly/idempotent annotations, providing valuable runtime behavior context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with natural-language queries and dense with functional detail. The multiple query phrasings are slightly redundant, but every sentence contributes to understanding the tool's scope.

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, it specifies returned fields (changes[], total_changes, pipeworx:// citations), source behavior, fallback logic, and parameter options. This is comprehensive for a 3-parameter tool.

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%, but the description enriches parameter meaning by clarifying `since` accepts ISO or relative shorthand and `value` accepts ticker or CIK. It also gives a typical monitoring recommendation.

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 precisely identifies a change feed for a company over a time window, with concrete examples of user intents. It explicitly distinguishes itself from entity_profile, which is the static-profile alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It names entity_profile as an explicit alternative for static profiles and recommends '30d' or '1m' for typical monitoring. It lacks a broader when-not-to-use comparison against recent_alerts or search, but the intended use case is well-scoped.

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

All Tatoeba 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

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