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Glama

Datagov

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

A4.7/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds significant behavioral context beyond annotations: it explains scoping by identifier, persistence differences between authenticated (persistent) and anonymous (24 hours) sessions, and storage mechanism (key-value pair). 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.

Conciseness5/5

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

The description is three sentences long, each serving a purpose: first defines the tool, second gives use cases, third explains storage details. It is front-loaded with the core action and uses no redundant words.

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 memory tool with no output schema, the description covers all essential aspects: what it saves, when to use, how it's stored (scoped, persistent), and how it relates to sibling tools (recall, forget). It is complete for the agent to understand the tool's role.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes both parameters with 100% coverage. The description adds value by providing examples for key (e.g., 'subject_property', 'target_ticker') and value ('findings, addresses, preferences, notes'), which helps the agent understand typical usage beyond schema types.

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 uses a specific verb ('Save data') and clearly identifies the resource ('data the agent will need to reuse later'). It distinguishes from sibling tools by naming recall and forget, and provides concrete examples of use cases (resolved ticker, target address, etc.), making the purpose unambiguous.

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 tells when to use the tool ('when you discover something worth carrying forward') and mentions related tools (recall, forget). It lacks explicit when-not-to-use guidance but provides clear context and pairing instructions, which is sufficient for most agents.

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

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TDQS

A3.9/5.0
Disambiguation2/5

Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research both overlap with the ask_pipeworx routing layer, and scan_competitor_ai_presence is essentially ai_visibility_check for multiple entities. The descriptions are very detailed, but an agent can still easily pick the wrong member of these clusters.

Naming Consistency3/5

Names are consistently snake_case and there are useful prefix families like ask_pipeworx_*, polymarket_*, and scan_*, but the grammatical convention is mixed: noun phrases like entity_profile, deep_research, and recent_changes sit alongside imperative verbs like get_dataset, resolve_entity, and subscribe. It is readable but not a single predictable verb_noun pattern.

Tool Count2/5

34 tools is above the 25+ threshold where a tool surface starts to feel bloated, and a large subset (prediction-market analytics, memory, AI-visibility, npm scanning, llms.txt generation) is extraneous to the Data.gov identity. The core data-query story could be told with far fewer top-level tools.

Completeness4/5

The broader Pipeworx data-access workflow is well covered: discovery, routing, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory all have dedicated tools. The Data.gov-specific piece is thin (search, metadata, organizations) but workable, with only minor gaps like no direct resource file download.

Resources