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Glama

Arcgis Loudoun

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

A4.7/5.0
Behavior5/5

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

The description adds critical behavioral details not available from annotations: memory is scoped by identifier, authenticated users get persistent memory, anonymous sessions retain for 24 hours, and it's a key-value store. These are important operational constraints that the readOnly/idempotent/destructive hints do not convey.

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 and front-loaded with the primary purpose, then usage guidance, then lifecycle details. Every sentence adds value, and there is no redundant or filler content.

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 2-parameter tool with full schema coverage and clear annotations, the description is complete. It covers purpose, usage, persistence semantics, and sibling relationships, leaving no ambiguity about how to use or invoke the tool.

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 description coverage is 100% and both parameters are well-defined with examples (e.g., key as 'subject_property', value as any text). The description only reiterates 'key-value pair' without adding new parameter meaning, 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 saves data for later reuse across conversations or sessions, using a specific verb ('Save') and resource ('data'). It also distinguishes itself from sibling tools recall and forget by explicitly naming them as partners.

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?

It explicitly says to use when discovering something worth carrying forward, gives concrete examples (ticker, address, preference, research subject), and states the alternatives: 'Pair with recall to retrieve later, forget to delete.' This provides clear when-to-use and when-not-to-use guidance.

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

A3.6/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap among data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and numerous Polymarket bet tools, which could cause confusion. Tool descriptions are detailed and help differentiate, but the diversity of domains requires careful reading.

Naming Consistency2/5

Tool names follow inconsistent patterns: some are snake_case verb_noun (query_layer, search_datasets), others are noun_verb (bet_research) or compound names (pipeworx_feedback, polymarket_arbitrage). There is no uniform convention, making it harder for agents to predict tool names.

Tool Count2/5

With 33 tools, the server feels overloaded for its apparent ArcGIS focus. Most tools are unrelated to ArcGIS (Polymarket, Pipeworx data, memory, subscriptions), suggesting a lack of scope. The count is high without a clear unifying purpose.

Completeness2/5

The tool surface is incomplete for any single domain. ArcGIS coverage is minimal (only query and schema), Pipeworx data tools are abundant but without a clear workflow, and Polymarket betting lacks order placement. The server tries to cover too many areas resulting in shallow coverage.

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