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Glama

Data Vermont

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 already indicate idempotent, non-destructive write. Description adds valuable behavioral details: scope by identifier, persistence duration (24h vs persistent), which are not in annotations.

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?

Five sentences with clear front-loading of purpose. Every sentence adds value: purpose, usage, storage mechanism, persistence, related tools. No redundancy.

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 write tool with full schema coverage and annotations, the description covers purpose, usage, behavior, persistence, and sibling relationships. No missing information observed.

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?

Input schema covers both parameters with descriptions (key examples, value text). Description doesn't add new semantics beyond schema, 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?

Description clearly states 'Save data the agent will need to reuse later' with specific verb (save) and resource (key-value memory). It distinguishes from siblings like 'recall' and 'forget' by naming them explicitly.

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 when-to-use guidance ('discover something worth carrying forward') with concrete examples (ticker, address, preference). Mentions pairing with recall and forget, offering clear alternatives.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket cluster (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) could cause confusion without careful reading. However, the detailed descriptions effectively differentiate each tool's specific role.

Naming Consistency5/5

All tool names follow consistent snake_case convention with a verb-noun pattern (e.g., compare_entities, recent_changes, resolve_entity). There are no mixed conventions or chaotic naming, making the set predictable and easy to navigate.

Tool Count4/5

At 34 tools, the set is extensive but justified by the server's broad scope covering Vermont open data, Pipeworx data platform, prediction markets, and utility features. Each tool earns its place, though the count is slightly high compared to typical servers.

Completeness4/5

The tool surface covers data retrieval, entity analysis, prediction markets, memory, subscriptions, feedback, and claim validation. Minor gaps exist (e.g., no direct compliance tools), but the set is comprehensive for its stated purpose of data integration and analysis.

Resources