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Glama

Ripe Stat

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

A4.9/5.0
Behavior5/5

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

Annotations provide idempotentHint=true and readOnlyHint=false. The description adds behavioral details: key-value storage scoped by identifier, persistence differences between authenticated users and anonymous sessions (24 hours). No contradictions.

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 concise sentences, front-loaded with purpose. Every sentence adds unique information without redundancy. No fluff.

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 simple 2-parameter tool with no output schema, the description covers all necessary aspects: purpose, usage, storage behavior, persistence, and cues for related tools. Fully adequate.

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 has 100% coverage with descriptions for 'key' and 'value'. The description adds value by providing example keys (e.g., 'subject_property', 'target_ticker'), but does not add significant additional semantics beyond the schema.

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' and provides concrete examples like 'a resolved ticker, a target address, a user preference'. This verb-resource combination distinguishes it from siblings like 'recall' and 'forget'.

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 states when to use: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' Also pairs with 'recall' and 'forget', guiding the agent on complementary tools.

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, especially in networking and domain-specific queries. However, the multiple `ask_pipeworx` variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and `deep_research` have overlapping functionality, which could cause occasional misselection.

Naming Consistency2/5

Tool names use inconsistent conventions: some are concatenated (geoloc, whois), some use underscores (abuse_contact, as_overview), and some are multi-word (scan_competitor_ai_presence, ask_pipeworx_grounded). No consistent pattern across the set.

Tool Count3/5

39 tools is high for a single server, but justified by the broad scope covering networking, finance, prediction markets, AI visibility, and more. However, many tools are meta-tools for the Pipeworx ecosystem, making the set feel slightly bloated.

Completeness5/5

The server covers an exceptionally wide range of domains comprehensively, with tools for networking, company data, FDA datasets, prediction markets, weather, news, and more. It includes memory and subscription management, leaving few obvious gaps for its stated data-retrieval purpose.

Resources