Skip to main content
Glama

Ooni

by io.github.pipeworx-io

Ooni App Blocking

ooni_app_blocking
Read-onlyIdempotent

Check whether a messaging or circumvention app is blocked in a country using OONI app-specific tests: WhatsApp, Telegram, Signal, Facebook Messenger, or Tor. Returns measurement counts and a blocking assessment, e.g. "is Telegram blocked in Iran", "is Tor reachable from Russia".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appYesOne of: whatsapp | telegram | signal | facebook_messenger | tor.
daysNoLookback window in days (default 30, max 365).
countryYes2-letter country code, e.g. "IR", "RU", "CN".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "app": "telegram",
+    "country": "IR"
+  },
+  {
+    "app": "tor",
+    "country": "CN",
+    "days": 60
+  }
+]
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds that it returns measurement counts and blocking assessment, but does not disclose other behaviors like data freshness, rate limits, or error handling.

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?

Two concise sentences with embedded examples. No unnecessary words, front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with annotations covering safety, the description adequately explains what it returns and how to use it. Could mention default days (30) but schema already includes that. No output schema, so return details are helpful.

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 covers 100% of parameters. Description adds examples and context for app and country, but does not significantly augment the schema's own descriptions (e.g., 'app' description is identical). Baseline 3 is appropriate.

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?

Description clearly states it checks app blocking for specific apps (WhatsApp, Telegram, etc.) and provides concrete examples like 'is Telegram blocked in Iran'. This distinguishes it from sibling tools like ooni_site_reachability.

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

Usage Guidelines3/5

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

Usage is implied by the description and examples, but there is no explicit guidance on when to use this tool vs alternatives like ooni_blocking_trend or ooni_measurements. No 'when not to use' statements.

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

All Ooni 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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but the ask_pipeworx family (including beta and grounded) and deep_research overlap in functionality, causing some ambiguity. The OONI tools are well-differentiated, and other tools are distinct.

Naming Consistency4/5

Naming predominantly follows a verb_noun pattern with underscores, but some tools like generate_llms_txt and pipeworx_feedback deviate slightly. Overall consistent with minor inconsistencies.

Tool Count4/5

35 tools is on the higher side but justified by the broad domain coverage (data lookup, censorship monitoring, prediction markets, memory, etc.). Each tool serves a specific purpose, making the count reasonable.

Completeness4/5

The tool surface covers core workflows comprehensively, including data retrieval, censorship analysis, prediction market evaluation, and memory management. Minor gaps exist (e.g., no subscription modification tool), but overall it's well-scoped.

Resources