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Glama

Data Govt Nz

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
Behavior5/5

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

Beyond the annotations (idempotentHint=true, destructiveHint=false), the description discloses key behavioral details: key-value storage scoped by identifier, persistence differences between authenticated (persistent) and anonymous (24 hours) sessions. It also describes the mutation semantics consistent with readOnlyHint=false, adding value beyond the structured fields.

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 four sentences, each serving a distinct purpose: what it does, when to use, how storage works, and related tools. It is front-loaded with the primary action and avoids redundant wording, making it efficient 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?

For a simple key-value store tool with no output schema, the description covers all essential aspects: purpose, usage triggers, persistence/scope details, and companion tools. The schema handles parameter documentation, so no critical context is 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% with detailed descriptions for both key and value parameters, so the description does not need to add parameter-level detail. The mention of 'key-value pair' and examples like 'resolved ticker' adds slight reinforcement, but essentially repeats what the schema already conveys, matching the baseline for full schema coverage.

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 opens with 'Save data the agent will need to reuse later', using the specific verb 'Save' and identifying the resource as agent memory. It explicitly distinguishes from siblings by stating 'Pair with recall to retrieve later, forget to delete', clearly separating this save operation from retrieval and deletion tools.

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?

The description provides a clear 'Use when' clause with concrete examples (resolved ticker, target address, user preference, research subject), giving strong contextual guidance. It mentions related tools (recall, forget) for alternatives, but does not explicitly state when NOT to use this tool, leaving a small gap in exclusion guidance.

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

All Data Govt Nz toolsInspect
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TDQS

B3.1/5.0
Disambiguation2/5

Tools span multiple unrelated domains (NZ open data, prediction markets, npm scanning, AI visibility, etc.). Within each domain, some tools are similar (e.g., multiple polymarket tools, multiple ask_pipeworx variants). The wide scope makes it hard for an agent to know which tool to use.

Naming Consistency2/5

Naming conventions are inconsistent: some use snake_case (ask_pipeworx, deep_research), some use underscore verbs (group_list, package_search), some are descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). No clear pattern.

Tool Count2/5

41 tools is high for a server named 'Data Govt Nz' but includes many unrelated tools (Polymarket, npm, AI visibility). The scope is sprawling; many tools seem extraneous to the core purpose.

Completeness2/5

The NZ open data portion is fairly complete (CRUD for groups, organizations, packages, tags), but other areas have only one or two tools (e.g., npm scanning, AI visibility). The overall surface is incomplete for a coherent domain.

Resources