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Glama

Rhea

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

A4.5/5.0
Behavior4/5

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

Annotations already provide idempotentHint and destructiveHint. The description adds important behavioral context: scoping by identifier, persistent vs session-based storage duration, and that it overwrites existing keys (implied by idempotent). Does not contradict annotations.

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, no fluff. Front-loaded with purpose, followed by usage guidance, then behavioral details. Every sentence adds value.

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 key-value store tool with no output schema, the description covers purpose, use case, storage details, and links to companion tools (recall, forget). No gaps identified.

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%, so parameters are fully documented. The description adds a naming convention example but no additional semantics beyond what the schema provides. Baseline 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 uses a specific verb ('Save') and resource ('data'), provides concrete examples, and distinguishes from sibling tools (recall, forget). It clearly states what the tool does and in what context.

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?

Explicitly says when to use ('when you discover something worth carrying forward') and mentions alternatives ('Pair with recall to retrieve later, forget to delete'). Provides clear guidance on when to invoke and when not.

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

All Rhea toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with only minor overlap between similar variants (e.g., ask_pipeworx variants) and related prediction market tools (bet_research vs polymarket_edges). Overall, an agent can reliably select the right tool.

Naming Consistency3/5

Tool names mix verb_noun (e.g., resolve_entity), noun_noun (e.g., entity_profile), and adjective_noun (e.g., recent_changes) patterns, plus a few single verbs. While readable, the lack of a uniform convention adds ambiguity.

Tool Count3/5

33 tools is large but justified by the broad domain coverage. The count is borderline high but still manageable with meta-tools (ask_pipeworx, deep_research) that reduce the effective surface.

Completeness4/5

The tool set covers a wide range of data sources and tasks, with meta-tools providing deep coverage. Minor gaps exist (e.g., no dedicated Kalshi-only tools, limited attention to non-Polymarket prediction markets), but the overall surface is thorough.

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