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Datagov Au

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

A4.1/5.0
Behavior4/5

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

Annotations already cover read/write/destructive/idempotent hints. The description adds valuable behavioral context beyond annotations: persistence scope ('scoped by your identifier'), retention differences ('Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'), and the key-value storage model. No contradiction with annotations found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences and every sentence contributes: purpose, when to use, storage details, and companion tools. It is slightly longer than the exemplary two-sentence case but remains efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple key-value write tool with no output schema, the description covers the essential context: what it does, when to use it, persistence semantics, and relationship to recall/forget. Minor gaps like overwrite behavior or size limits are not material for typical usage.

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 documentation covers 100% of parameters with clear descriptions and examples. The description adds conceptual framing (key-value pair, scoping) and example use contexts, but does not meaningfully extend the parameter-level semantics already present in 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 clearly states the tool's function: 'Save data the agent will need to reuse later' with a specific resource (key-value pair). It distinguishes itself from sibling tools like recall and forget by explicitly referencing them as complementary operations.

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 concrete use cases ('a resolved ticker, a target address, a user preference, a research subject') and guidance to use it when avoiding re-lookup is important. It mentions pairing with recall and forget, but does not explicitly state when not to use this tool versus alternatives.

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

All Datagov Au toolsInspect
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TDQS

B3.2/5.0
Disambiguation3/5

Most tools have detailed descriptions and distinct purposes, but there is overlap among the question-answering tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the six Polymarket-related tools. An agent could reasonably misselect when choosing between the pipeworx variants or between entity_profile and recent_changes.

Naming Consistency4/5

Tool names consistently use snake_case and most follow a verb_noun or noun pattern, with clear domain prefixes like pipeworx_ and polymarket_. Minor deviations include single-word verbs (search, subscribe) and adjective_noun names (recent_alerts, deep_research), but the overall pattern is predictable.

Tool Count2/5

With 37 tools, the server is well beyond the 25+ threshold, and the bulk of them are unrelated to the 'Datagov Au' name. The actual data.gov.au catalogue is served by just six tools (search, package, resource, organizations, groups, tags), while the rest are Pipeworx, Polymarket, memory, subscription, and utility tools — a grossly bloated and miscategorized surface.

Completeness2/5

For a server named Datagov Au, the CKAN tools only cover read-only catalogue lookups; there are no tools for data.gov.au-specific updates, queries, or subscriptions. Conversely, the Pipeworx router exposes access to 5,502 internal tools, but many of those are not directly callable as MCP tools, leaving a confusing gap where discover_tools returns tools the agent cannot actually invoke.

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