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Glama

Missouri License Offices

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

A5/5.0
Behavior5/5

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

The description adds critical behavioral context beyond annotations: 'scoped by your identifier,' 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' No contradictions with annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true).

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?

Four sentences, each serving a distinct purpose: purpose, usage guide, behavioral details, and tool pairings. No fluff or repetition. Front-loaded with the core action.

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?

Given the simple parameter set (2 required strings) and no output schema, the description covers all necessary aspects: purpose, when to use, persistence behavior, scoping, and sibling tool relationships. No gaps remain.

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

Parameters5/5

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

The input schema already provides descriptions for both parameters (100% coverage). The description adds value by suggesting example keys ('subject_property', 'target_ticker') and the nature of values ('findings, addresses, preferences, notes'), clarifying usage 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 states the verb 'Save data' and the resource 'key-value pair', and distinguishes from siblings recall and forget. It specifies the tool is for storing reusable data across sessions, which is specific and unambiguous.

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?

The description explicitly advises when to use: 'when you discover something worth carrying forward.' It also mentions complementary tools: 'Pair with recall to retrieve later, forget to delete.' This provides excellent guidance on tool selection.

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
Disambiguation1/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route natural-language data questions, while entity_profile, recent_changes, compare_entities, and resolve_entity overlap around company data. An agent cannot reliably distinguish which retrieval entry point to choose.

Naming Consistency4/5

Tool names are almost uniformly lowercase snake_case and mostly follow a recognizable verb_noun or domain-prefixed pattern (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*, recent_*, subscribe/unsubscribe). A few names like deep_research, entity_profile, and bet_research break the verb-first style, but there is no chaotic convention mixing.

Tool Count2/5

32 tools is too many for the apparent scope, and more importantly only one tool (mo_dmv_license_offices) matches the server name 'Missouri License Offices.' The other 31 tools form a general data-research, prediction-market, memory, and subscription platform that has little to do with the stated purpose.

Completeness4/5

Judged as the broad Pipeworx-style research platform the descriptions reveal, the surface is quite complete: simple and grounded querying, deep multi-source research, claim verification, entity resolution, profiles, comparisons, change feeds, discovery, subscriptions, alerts, and memory. For the literal Missouri license-office purpose, however, only the single lookup tool is present, which drags down completeness despite that tool being reasonably thorough.

Resources