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Glama

Datagov Uk

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
Behavior4/5

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

Adds context beyond annotations: memory scoping by identifier, persistence duration (24h for anonymous, persistent for authenticated). Annotations indicate idempotent and non-destructive, which aligns.

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, front-loaded with purpose, no wasted words. Efficient and clear.

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 with 2 parameters and no output schema, the description is complete: explains when, what, and persistence behavior.

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%, so description adds marginal value. However, it does clarify scoping (by identifier) and the nature of value ('any text'). 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 clearly states the tool saves data for reuse across conversations or sessions, using specific verbs like 'save' and 'store'. It distinguishes itself 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 Guidelines5/5

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

Provides explicit guidance on when to use ('when you discover something worth carrying forward') and mentions alternatives ('pair with recall to retrieve later, forget to delete').

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

All Datagov Uk toolsInspect
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TDQS

A3.6/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, which all route questions to the same source catalog and could be easily confused. Similarly, ai_visibility_check and scan_competitor_ai_presence overlap, and the many polymarket_* tools require careful reading to distinguish. The descriptions are detailed enough to help, but the set is not cleanly separable.

Naming Consistency3/5

All names use lowercase snake_case, which is consistent in style, but the pattern is mixed: some tools use verb_noun (search_datasets, list_organizations, validate_claim), while others are bare nouns or noun phrases (entity_profile, dataset_details, recent_alerts, polymarket_edges). This makes the naming readable but not predictable, and it lacks a single clear convention.

Tool Count2/5

At 35 tools, this is a heavy surface, and most of them are unrelated to the server's apparent 'Datagov Uk' purpose—only search_datasets, dataset_details, list_organizations, and organization_details actually serve data.gov.uk. The rest are a general Pipeworx/prediction-market/AI-visibility toolkit bolted onto a government data server, making the count inappropriate for the stated scope.

Completeness2/5

For the nominal data.gov.uk domain, the four CKAN tools cover search, dataset details, and organization listing, but there is no create/update/delete (datasets are open data, so that is acceptable) and no direct resource download or preview helper despite resource links being returned. More importantly, the overwhelming majority of tools address unrelated domains, so the server's actual coverage is scattered and does not form a coherent complete surface for any single stated purpose.

Resources