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Glama

Ooni

by io.github.pipeworx-io

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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

A4.8/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true (safe to repeat) and destructiveHint=false. The description adds critical context: data is key-value scoped by identifier, persistent for authenticated users, and 24-hour retention for anonymous sessions. No contradictions.

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?

4 sentences covering purpose, usage, behavior, and pairing recommendations. Every sentence adds value, front-loaded with the primary function.

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?

No output schema, but the tool is a write operation; the description adequately explains behavior (persistence, scoping). It pairs with recall/forget but could mention that no return value is expected.

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 clear parameter descriptions. The description reinforces the key-value nature but does not add extra semantics. A minor deduction for not providing more examples or format constraints for keys.

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 starts with a clear verb+resource ('Save data') and specifies the scope ('across this conversation or across sessions'). It distinguishes itself from sibling tools like recall and forget by explicitly naming them as complementary.

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?

The description explicitly states when to use the tool ('when you discover something worth carrying forward...') and what kind of data to store (tickers, addresses, preferences, subjects). It also names the sibling tools recall and forget for retrieval and deletion.

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

All Ooni toolsInspect
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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