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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

A4.9/5.0
Behavior5/5

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

Annotations already indicate idempotent and non-destructive behavior, but the description adds essential context: memory is 'scoped by your identifier', persistent for authenticated users, and retained only 24 hours for anonymous sessions. It also clarifies overwrite semantics (key-value pair) which goes beyond the raw 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?

The description is compact and front-loaded: first sentence states the purpose, second gives usage guidance, third explains storage semantics, and fourth covers persistence and companion tools. Every sentence adds new information 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 memory-write tool, the description covers purpose, when to use, persistence behavior, and how to complement it with recall/forget. The schema fully documents both parameters, and no output schema is needed for a write operation. The description is sufficient.

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?

The schema already describes both key and value with examples, so baseline is 3. The description enriches semantics by explaining that the key-value pair is scoped by the caller's identifier and gives concrete usage examples (ticker, address, preference, research subject), which clarifies what should be stored in each parameter.

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 opens with 'Save data the agent will need to reuse later' — a specific verb+resource that clearly identifies the tool's function. It also distinguishes from siblings by naming companion tools 'recall' and 'forget' and describing the memory lifecycle.

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: 'Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject)' and provides an isolation criterion ('so you don't have to look it up again'). It also names alternatives for retrieval and deletion, making the choice clear.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but ask_pipeworx and ask_pipeworx_grounded are very similar and could cause confusion. The multiple Polymarket tools are differentiated by their specific functions.

Naming Consistency3/5

Tool names are a mix of verb_noun (e.g., ask_pipeworx, get_verse) and noun_verb (e.g., polymarket_arbitrage, ai_visibility_check). While all use snake_case, the pattern is inconsistent.

Tool Count4/5

With 33 tools, the server is comprehensive but slightly large. Each tool seems justified, covering multiple domains like company data, prediction markets, Bible, and memory.

Completeness5/5

The tool surface is highly complete for a universal data server, including financials, drugs, patents, news, real estate, and more. Meta-tools like discover_tools and suggest_questions further enhance usability.