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Glama

Rba

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

A4.7/5.0
Behavior5/5

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

The description discloses behavioral traits beyond annotations: persistence scoped by identifier, retention differences between authenticated and anonymous sessions, and the key-value storage model. This adds valuable context that annotations (readOnlyHint, idempotentHint) do not convey.

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 three sentences, each sentence serving a distinct purpose: stating the core function, providing usage context, and explaining storage/retention semantics. No filler or redundant information; it is appropriately concise and well-structured.

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 two-parameter schema and existing annotations, the description covers all essential aspects: what it does, when to use it, how data is stored, and how it relates to sibling tools. No return value is needed, and no significant gaps remain.

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%, so baseline is 3. The description adds minimal parameter-specific meaning beyond the schema; it mentions 'key-value pair' but does not elaborate on the parameters themselves. Thus it doesn't exceed the baseline.

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 function with a specific verb ('Save') and resource ('data the agent will need to reuse later'). It also distinguishes itself from siblings by mentioning 'recall' and 'forget' as companion tools, making the purpose unambiguous.

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 specifies when to use the tool: 'Use when you discover something worth carrying forward...' and provides concrete examples. It also names alternatives ('Pair with recall to retrieve later, forget to delete'), giving clear usage guidance.

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

All Rba toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as entry points for data questions, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. Entity-focused tools (entity_profile, compare_entities, recent_changes, ai_visibility_check, scan_competitor_ai_presence) also blur together even with lengthy descriptions.

Naming Consistency4/5

All tools use lowercase snake_case, which is consistent and readable. There is some variation between verb-first names (discover_tools, resolve_entity) and noun-first names (entity_profile, polymarket_arbitrage), plus the ask_pipeworx variant family, but the pattern is predictable overall.

Tool Count2/5

35 tools is heavy, and the scope sprawls far beyond the 'Rba' server name: only 4 tools relate to the Reserve Bank of Australia, while the rest cover a universal data router, prediction markets, memory, subscriptions, AI visibility, npm scanning, and more. Several meta-tools (ask_pipeworx, deep_research, discover_tools, suggest_questions) duplicate the discovery/routing role, making the count feel inflated.

Completeness3/5

For the RBA-specific subdomain, coverage is solid: directory lookup, series fetching, cash rate, and exchange rates. For the broader data-research domain most tools imply, coverage is quite comprehensive (routing, grounded answers, entity resolution, fact-checking, monitoring, memory), but the server's stated identity is unclear, and the beta duplicate tool adds noise rather than filling a real gap.

Resources