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Glama

newton

by io.github.pipeworx-io

Remember

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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

A5/5.0
Behavior5/5

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

Annotations indicate non-readonly, non-destructive, and idempotent. The description adds key behavioral context: persistence details ('Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours') and scoping ('by your identifier'). No contradiction.

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?

Three sentences pack purpose, usage, storage model, persistence, and companions without repetition. Every sentence adds value and the structure is logical.

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, the description covers the tool's full lifecycle: when to save, how it's stored, persistence rules, and how to retrieve/delete. Output schema is absent and unnecessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description enriches both parameters: key examples ('subject_property', 'target_ticker') and value clarification ('any text — findings, addresses, preferences, notes').

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 'Save data the agent will need to reuse later' with a specific verb and resource. It also specifies scope ('across this conversation or across sessions') and distinguishes from siblings like recall (retrieve) and forget (delete).

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 'Use when you discover something worth carrying forward' with concrete examples (ticker, address, preference). It also gives direct guidance on complementary tools: 'Pair with recall to retrieve later, forget to delete.'

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ways to get entity information (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities) and numerous prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Despite detailed descriptions, the boundaries are unclear, making it difficult for an agent to distinguish between them.

Naming Consistency4/5

Tool names mostly follow a snake_case convention and are generally descriptive. Minor inconsistencies exist, such as 'discover_tools' vs. 'scan_competitor_ai_presence' and a few single-word verbs like 'derive' and 'remember'. Overall, the pattern is predictable and readable.

Tool Count2/5

With 34 tools, the server is heavy for a single server. The scope is very broad, covering math, memory, data retrieval, prediction markets, and more. While each tool has a specific purpose, the high count suggests a lack of focus and could overwhelm an agent.

Completeness4/5

The server offers extensive coverage for data retrieval, entity lookup, comparison, research, and prediction markets. Minor gaps exist, such as missing advanced math operations (e.g., solving equations) and some niche data sources, but the core workflows are well-covered with tools like ask_pipeworx and deep_research.

Resources