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Glama

Uk Ons

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

A4.3/5.0
Behavior5/5

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

Annotations provide idempotentHint and readOnlyHint; description adds key behavioral details: scoped by identifier, persistence differences for authenticated vs anonymous users (24-hour retention), and data type (key-value, any text). No contradictions.

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?

Description is a single paragraph but front-loaded with purpose, followed by usage context and retention details. Every sentence adds value, though could be slightly more concise.

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?

With no output schema, description covers persistence, scoping, and pairing. For a simple storage tool with two required params, it provides sufficient context for correct invocation.

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 has 100% coverage with clear descriptions for key (examples) and value (any text). Description reinforces but does not add new semantic meaning beyond schema examples.

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 uses specific verb 'Save data' and resource 'key-value pair'. It explicitly distinguishes from sibling tools 'recall' and 'forget' by mentioning them as complementary actions.

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?

States when to use: 'when you discover something worth carrying forward'. Mentions pairing with recall/forget, but does not explicitly state when not to use or provide alternatives.

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

All Uk Ons toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have clearly separated jobs, but the set is crowded with overlapping research/query entry points: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the Polymarket/research cluster has fuzzy boundaries. Detailed descriptions help, but an agent can still easily mis-select among these tools.

Naming Consistency3/5

Names are consistently snake_case and readable, but they mix verb-led commands (get_dataset, list_editions, validate_claim) with noun-led descriptive names (entity_profile, polymarket_edges, recent_changes) and a version-suffixed duplicate (ask_pipeworx_beta). The ONS tools also lack a shared ons_ prefix aside from ons_timeseries, so the naming is more a collection of conventions than one predictable pattern.

Tool Count2/5

37 tools is well past the heavy range for a server whose name suggests a focused UK ONS statistics surface; only about six tools actually serve ONS data, while the rest span memory, subscriptions, prediction markets, npm scanning, AI visibility, and general Pipeworx plumbing. The count is not an extreme 50+ sprawl, but it is too many for a coherent scope.

Completeness4/5

The core ONS read workflow is well covered: catalog discovery through list_datasets, dataset/edition/version metadata, filtered get_observations, and classic time series via ons_timeseries. Minor gaps exist, such as no dedicated dataset search and some peripheral one-off features like scan_dependency or generate_llms_txt, but agents can work around them.

Resources