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Glama

Oregon DMV

by io.github.pipeworx-io

Remember

remember
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
Behavior4/5

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

Beyond annotations, description reveals persistence details (scoped by identifier, lifetime for authenticated vs anonymous) and implies mutation. Could clarify overwrite behavior, but idempotentHint covers idempotency.

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?

Front-loaded with main purpose, each sentence adds value. Slightly verbose but still concise overall.

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?

Covers purpose, usage, behavior, and pairing with related tools. No output schema needed; description is sufficient for a simple write operation.

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 minimal parameter info beyond usage examples. The examples are helpful but not essential given schema clarity.

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 the action ('save data') and the resource ('key-value pair'), and distinguishes from sibling tools '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 guidance on when to use ('when you discover something worth carrying forward') and pairs with 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 Oregon DMV toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools occupy the same functional space: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the Polymarket cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) heavily overlaps in purpose. The only clearly separated tools are the two DMV-specific ones, but they are drowned out by ambiguous data-query and prediction-market tools.

Naming Consistency4/5

Tool names mostly follow a predictable snake_case verb_noun pattern such as list_subscriptions, resolve_entity, validate_claim, and the polymarket_* / or_dmv_* prefixes are consistent. Minor deviations like bet_research, pipeworx_feedback, and ask_pipeworx_beta break the pattern slightly, but the overall style is coherent and readable.

Tool Count1/5

A server named 'Oregon DMV' exposes 33 tools, only 2 of which relate to DMV office locations and wait times. The other 31 tools form a broad general-purpose data and prediction-market platform, making the count and scope an extreme mismatch for the stated server identity.

Completeness1/5

For an Oregon DMV server, the surface is severely incomplete: there are no tools for appointments, forms, fees, licensing, registration, or services. The two DMV tools cover only office addresses and live wait times, covering a tiny slice of the domain implied by the server name.

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