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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 six ca_dmv_* tools are clearly distinct, but the set also contains near-duplicates like ask_pipeworx and ask_pipeworx_beta (currently functionally identical), five overlapping Polymarket analysis tools, and two overlapping AI-visibility probes. An agent would frequently struggle to select the right tool among these overlapping families.

Naming Consistency3/5

Names are mostly lowercase snake_case and individually readable, but conventions are mixed: ca_dmv_* prefix vs. bare verbs like forget, compound names like generate_llms_txt, and near-identical pairs like polymarket_edges vs. polymarket_edge_tracker. The pattern is inconsistent enough to impede quick scanning.

Tool Count1/5

37 tools for a server named 'California DMV' is an extreme scope mismatch: only 6 tools relate to DMV while 31 are general-purpose research, prediction-market, memory, and subscription tools. The DMV-specific subset would be well-scoped at 6 tools, but the bundled platform makes the server feel bloated and off-topic.

Completeness2/5

The DMV portion covers licenses, registrations, EV adoption, offices, forms, and insurance codes, but misses common DMV needs like title transfers, fee estimates, or appointment booking. The broader research platform is extensive, but that does not compensate for the server's stated purpose, leaving the DMV surface with significant gaps.

Resources