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Glama

Ine Pt

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare idempotentHint=true, non-destructive. The description adds valuable context: scoping by identifier, persistence differences (authenticated vs anonymous, 24-hour limit), and pairs with recall/forget. No contradiction with 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?

Three sentences, none wasted. Front-loaded with purpose, then usage, then storage details. Efficient and clear.

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?

Given the tool's low complexity (2 required params, no output schema), the description fully covers purpose, usage, persistence, and relationship to sibling tools. No gaps.

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%, but the description adds examples for key and value (e.g., 'subject_property', 'any text'), which enriches the semantics beyond the schema's bare descriptions. A 5 would require more detailed parameter constraints.

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 saves data for later reuse, with specific verbs ('Save', 'Store') and resource ('key-value pair'). It distinguishes from siblings recall and forget by mentioning them explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit context for when to use (discover something worth carrying forward) and mentions paired tools (recall, forget). However, it lacks explicit 'when not to use' statements, which would make it a 5.

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, with detailed descriptions. However, a few tools like 'discover_tools' and 'suggest_questions' both serve exploratory functions and could cause confusion. Similarly, 'ask_pipeworx' and 'deep_research' overlap in scope but are differentiated by depth and account requirements. Overall, an agent can typically pick the right tool, but a few pairs require careful reading.

Naming Consistency3/5

All names use snake_case and are generally readable, but the convention varies: some are verb_noun (e.g., 'resolve_entity'), some are noun_noun (e.g., 'entity_profile'), and a few are just verbs (e.g., 'remember', 'forget'). The 'polymarket_' prefix helps group related tools, but the diversity in patterns slightly reduces predictability.

Tool Count4/5

With 32 tools, the set is slightly large but justified by the wide range of functionality: data querying, prediction markets, memory, subscriptions, and utilities. Each tool serves a distinct purpose, and the count is not excessive given the server's role as a gateway to thousands of data sources. It feels well-scoped for its domain.

Completeness4/5

The tool set covers most essential operations: querying data, entity profiles, comparisons, subscriptions, memory, and onboarding. Minor gaps exist, such as the lack of a generic subscription for all data changes or a way to list all available data packs directly. However, 'discover_tools' partially addresses this. Overall, the surface is comprehensive for the server's purpose.

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