Skip to main content
Glama

Sarb Za

by io.github.pipeworx-io

Remember

remember
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

A4.3/5.0
Behavior4/5

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

Adds context beyond annotations: scoping by identifier, persistence differences (authenticated vs anonymous), and 24-hour retention. Idempotent and non-destructive nature is consistent 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?

Concise single paragraph, front-loaded with purpose, every sentence adds value. No wasted words.

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 simple 2-param schema and no output schema, the description fully explains behavior, usage, persistence, and relationships with sibling tools (recall, forget). No gaps for this tool's complexity.

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 coverage is 100% with clear descriptions, so baseline is 3. The description adds examples and scoping context but does not significantly enhance parameter meaning 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 uses a specific verb (save) and resource (data for reuse across conversation/sessions) and distinguishes from siblings by explicitly naming paired tools (recall, forget). Concrete examples (resolved ticker, target address) clarify scope.

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?

Clearly states when to use: 'when you discover something worth carrying forward' and implies avoiding repeated lookups. Mentions paired tools for retrieval and deletion but lacks explicit when-not-to-use guidance.

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

All Sarb Za toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools have overlapping or redundant purposes, most notably ask_pipeworx and ask_pipeworx_beta (currently identical), and the cluster of Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) which all surface trading opportunities. While the lengthy descriptions help, an agent could easily select the wrong tool.

Naming Consistency4/5

All tool names use snake_case with a readable verb/noun structure, and there are no casing inconsistencies. However, the verb-first vs noun-first pattern is not uniformly applied (e.g., ask_pipeworx vs sarb_timeseries vs polymarket_edge_tracker), so it's mostly consistent with minor deviations.

Tool Count2/5

At 36 tools, the set is large and exceeds the 25-tool threshold; the broad scope justifies some volume but the presence of duplicate/overlapping tools (ask_pipeworx_beta, multiple Polymarket scanners) makes the count feel inflated.

Completeness4/5

The set covers a wide domain — SARB data, company research, prediction markets, AI visibility, memory, and subscriptions — with a good lifecycle for most features. Minor gaps exist (no subscription update, no bulk data export), but the core workflows are well covered.

Resources