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Glama

California DMV

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
Behavior4/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds value by explaining the key-value scoping, session persistence (24h for anonymous), and that it is a write operation (readOnlyHint=false) without contradicting 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?

The description is concise at four sentences, front-loaded with the main purpose, and every sentence adds value. No redundancy or wasted 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 tool with two string parameters and no output schema, the description covers purpose, usage guidance, behavioral context, and pairing with siblings. It is completely adequate.

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% with descriptions for both parameters. The description reinforces the schema with examples (e.g., 'subject_property') but does not add significant new semantic meaning beyond the schema.

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 specifies the verb 'Save', the resource 'data', and the context 'reuse later across sessions'. It explicitly distinguishes from sibling tools by mentioning 'recall' and 'forget' for retrieval and deletion.

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?

It states when to use: 'when you discover something worth carrying forward'. It also provides scoping and persistence details (authenticated vs anonymous). However, it does not explicitly exclude use cases or mention when not to use.

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

A3.5/5.0
Disambiguation2/5

The DMV-specific tools are individually distinct, but they are buried among ~30 unrelated Pipeworx utilities with several overlapping pairs (ask_pipeworx variants, entity_profile/compare_entities/recent_changes, and the polymarket suite). An agent pointed at this server cannot reliably tell which tools belong to the California DMV domain versus the general data platform.

Naming Consistency3/5

Most tools use lowercase snake_case, and the ca_dmv_* family is consistent, but there is no coherent semantic pattern across the set: some names are noun phrases (entity_profile), some verb phrases (discover_tools, validate_claim), and many are product-specific prefixes (ask_pipeworx, polymarket_*). The formatting is consistent, but the naming conventions are mixed.

Tool Count1/5

37 tools is far too many for a California DMV server; only 6 are actually DMV-specific. The remaining ~30 tools cover general Pipeworx data lookup, prediction markets, memory, subscriptions, and AI visibility, which belong in a separate server entirely.

Completeness2/5

For the stated DMV scope, the surface is thin: it covers registrations, licenses, offices, forms, insurance codes, and EV adoption, but misses common DMV queries like registration fees, title/status lookups, and appointment or transaction data. The 30 unrelated tools do not fill these domain gaps and instead obscure them.

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