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Glama

Nbp Pl

by io.github.pipeworx-io

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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

A4.5/5.0
Behavior4/5

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

Annotations declare idempotentHint=true and destructiveHint=false, so description doesn't repeat that. But adds value with persistence details: scoped by identifier, 24-hour retention for anonymous, persistent for authenticated users. 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, front-loaded with purpose, then usage guidelines, then behavioral details. No unnecessary words; every sentence earns its place.

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 2 required parameters, no output schema, the description covers purpose, usage, persistence, and pairing with siblings. It provides all necessary context for an agent to use this tool correctly.

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 for key and value. Description mentions key-value pair and gives examples but does not add significant new semantics beyond what schema provides.

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?

Description clearly states 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes itself from sibling tools 'recall' and 'forget' by explicitly mentioning them as companions.

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 tells when to use: 'Use when you discover something worth carrying forward...' and provides examples like resolved ticker, target address. Also instructs to pair with recall/forget, offering clear alternatives.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed routing guidance, but the three ask_pipeworx variants and overlapping company-focused tools (entity_profile vs compare_entities vs recent_changes) create some ambiguity. The descriptions are thorough enough that an agent can usually select correctly.

Naming Consistency4/5

All names are snake_case and mostly descriptive, but they mix verb-first (ask_pipeworx, discover_tools), noun-first (entity_profile, gold_price), and domain-prefixed (polymarket_*, pipeworx_*) patterns. The style is consistent enough to be predictable, though not uniform verb_noun.

Tool Count2/5

34 tools is well past the 25-tool threshold for "too many," and the server name (Nbp Pl) suggests a narrow Polish-bank scope while most tools cover unrelated domains like prediction markets, dependency scanning, and AI visibility. The breadth makes the set feel over-stuffed and unfocused.

Completeness4/5

The toolset covers a remarkably complete data-research workflow: general querying, grounded answers, deep research, entity resolution, profiles, comparisons, claim validation, change feeds, discovery, subscriptions, and memory. Minor gaps like subscription updating or a direct source catalog exist, but agents can work around them.

Resources