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Glama

Maryland Open Data

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

A4.4/5.0
Behavior5/5

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

Annotations provide idempotentHint=true and destructiveHint=false. The description adds valuable context: memory is scoped by identifier, authenticated users get persistence, anonymous sessions have a 24-hour TTL. No contradictions with 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 no wasted words. Front-loaded with core purpose, then usage, scope, and technical details. Every sentence earns its place.

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?

For a write tool with 2 parameters and clear semantics, description covers purpose, usage, persistence, and pairing. Missing: what happens on duplicate key, whether it returns success/confirmation, but overall sufficient.

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 clear descriptions for both parameters. The description refers to 'key-value pair' but does not add meaning beyond the schema. Examples in description align with schema examples, 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?

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later.' It uses a specific verb ('Save') and resource ('data'), and distinguishes from siblings by mentioning pairing with 'recall' and 'forget'.

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?

The description explicitly says 'Use when you discover something worth carrying forward' and lists concrete examples. It implies alternatives by naming sibling tools for retrieval and deletion, but does not explicitly state 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.

All Maryland Open Data toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

Tools are generally distinct in purpose, but the server name 'Maryland Open Data' conflicts with the inclusion of many unrelated Pipeworx tools (e.g., prediction market tools). This creates ambiguity about the server's actual domain, making it hard for agents to know what to expect.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others are plain (datasets, query), and some are descriptive phrases (ask_pipeworx_grounded). No consistent verb_noun pattern emerges, leading to a chaotic feel.

Tool Count2/5

33 tools is high, and the majority are unrelated to Maryland Open Data, suggesting scope creep. The server tries to be a general-purpose data platform but is named after a specific dataset, making the count feel excessive and unfocused.

Completeness3/5

The Maryland Open Data subset is minimal (3 tools: datasets, metadata, query), lacking update/delete/CRUD operations. The broader set includes many query and analysis tools, but the server's stated purpose is not fully covered.

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