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Glama

Datagov Au

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "key": "user_research_topic"
+  },
+  {}
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable context beyond annotations: it explains the scoping (anonymous IP, BYO key hash, or account ID) and the dual behavior (retrieve vs list). It does not detail return format, but that is acceptable given the simple nature and lack of output schema.

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 concise and well-structured: three sentences, front-loaded with the core action, followed by use case, scope, and pairing. Every sentence adds value, and there is no wasted wording.

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 tool with one optional parameter and rich annotations, the description covers all essential aspects: purpose, usage context, scope, and related tools. It effectively tells the agent when and how to use it, and the lack of an output schema is mitigated by the inherent clarity of the promised return (value or list).

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 description coverage is 100%, and the parameter 'key' is already described in the schema as 'Memory key to retrieve (omit to list all keys).' The description largely restates this information without adding new semantics. It does provide examples of key content (target ticker, address), but that is more about usage than parameter format, so baseline 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?

The description explicitly states the tool's action: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It clearly identifies the resource (previously saved memories) and the two modes of operation. It also distinguishes from sibling tools by mentioning 'remember' and 'forget' as paired operations.

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 provides explicit guidance on when to use: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also names alternatives and complements: 'Pair with remember to save, forget to delete.' This gives clear context for selection among sibling tools.

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.

Resources