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Glama

Digitalnz

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)

TDQS

A4.3/5.0
Behavior4/5

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

Adds context beyond annotations: 'scoped by your identifier', 'persistent memory for authenticated users', '24-hour retention for anonymous sessions'. Transparent about scoping and persistence.

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?

Two sentences with no fluff. First sentence states core purpose, second provides usage context and sibling references. Well-structured and front-loaded.

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?

Fully sufficient for a simple 2-parameter tool. Annotations cover read/write/destructive hints, and description covers scoping and retention. No output schema needed; nothing missing.

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% (all parameters described). Description adds only examples of keys ('subject_property', 'target_ticker'), which provides marginal extra context. Baseline of 3 is appropriate.

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?

Clearly states 'Save data the agent will need to reuse later' and specifies the action (save) and resource (key-value pairs). Distinguishes from sibling tools '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 Guidelines4/5

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

Provides explicit when to use: 'when you discover something worth carrying forward'. Mentions pairing with recall and forget. Lacks explicit when-not scenarios, but the usage context is well defined.

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

A3.9/5.0
Disambiguation2/5

Several tools are difficult to distinguish: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx_grounded, deep_research, and ask_pipeworx have fuzzy boundaries. The polymarket_* family plus bet_research also overlap heavily, requiring agents to carefully parse long descriptions to avoid misselection.

Naming Consistency4/5

Naming is predominantly snake_case with a verb-first pattern (ask_, search, subscribe, unsubscribe, list_) and clear prefix families like polymarket_ and pipeworx_. Minor deviations like ai_visibility_check and entity_profile use noun-first phrasing, but the overall pattern is still predictable and readable.

Tool Count2/5

33 tools is heavy for any single server, and the count is especially inappropriate given the server is named Digitalnz but only two tools (search, record) serve that domain. The rest form an unrelated grab-bag of data research, prediction-market, AI-visibility, memory, and utility tools.

Completeness3/5

The research workflow is fairly well covered: ask/grounded/deep modes, entity resolution, comparisons, claim validation, subscriptions, and alerts all exist. However, the DigitalNZ surface is nearly absent—just search and record—which is a significant gap for the declared server name, while other domains like AI visibility and npm dependencies are isolated one-offs.

Resources