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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. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description goes beyond annotations by disclosing scoping ('scoped by your identifier'), persistence details ('Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'), and the full lifecycle ('Pair with recall to retrieve later, forget to delete'). This adds significant behavioral context that annotations alone do not cover.

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?

The description is concise (5 sentences) and front-loaded with the core purpose in the first sentence. Every sentence adds value—usage context, storage behavior, persistence, and related tools—without redundancy.

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 write tool with 2 well-documented parameters and no output schema, the description is complete. It covers purpose, usage, scoping, persistence, and the related lifecycle tools (recall/forget). No critical gaps remain.

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 description coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond the schema, but it does reinforce the key-value structure. This is acceptable given the schema already fully documents both parameters.

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 purpose: 'Save data the agent will need to reuse later.' It uses a specific verb ('save') and resource ('data' as key-value pairs). It also distinguishes from siblings by explicitly naming 'recall' and 'forget' for related operations.

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 gives 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 names alternatives for related actions: 'Pair with recall to retrieve later, forget to delete.' This clearly contextualizes when to use this tool versus siblings.

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

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose and description, with no overlap. For example, ask_pipeworx vs ask_pipeworx_grounded are differentiated by hallucination resistance, and all Polymarket tools have unique roles.

Naming Consistency4/5

Tool names follow a mostly consistent snake_case pattern with descriptive verb_noun structures (e.g., ask_pipeworx, get_intensity, resolve_entity). Minor deviations like generate_llms_txt and pipeworx_feedback do not cause confusion.

Tool Count3/5

33 tools is high for a single server, covering many domains (Pipeworx, Polymarket, electricity, memory, npm). While well-organized, the broad scope may feel bloated for a focused use case.

Completeness4/5

Within each subdomain, the tool surface is complete: Pipeworx has query, research, entities, subscriptions; Polymarket has arbitrage, edges, fill risk; electricity has mix and intensity. Only minor gaps exist (e.g., missing PyPI support in scan_dependency).