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Glama

Nps

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

A4.8/5.0
Behavior5/5

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

The description adds behavioral context beyond annotations: stored as key-value, scoped by identifier, 24-hour retention for anonymous sessions, persistent for authenticated users. Annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true) are not contradicted.

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 (4 sentences), front-loaded with the core purpose, and efficiently covers usage, behavior, and pairing with siblings. No redundant or unnecessary information.

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?

Given no output schema, the description adequately explains what the tool does and its persistence behavior. However, it does not mention return values (e.g., success indicator) or error conditions, which would be helpful for completeness. Still, it is sufficient for an idempotent write tool.

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

Parameters4/5

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

The input schema provides 100% coverage with descriptions for both parameters (key, value). The description adds value by providing concrete examples for key (subject_property, target_ticker, user_preference) and clarifying that value can be any text, reinforcing schema details.

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 lists specific use cases (resolved ticker, target address, user preference, research subject) and distinguishes it from sibling tools like 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 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 the tool: 'Use when you discover something worth carrying forward.' It also explains the scope (scoped by identifier) and persistence differences (authenticated users vs anonymous sessions). Additionally, it mentions pairing with recall and forget for retrieval and deletion.

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
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., NPS park tools vs. Pipeworx queries vs. Polymarket analysis). Some overlap exists between ask_pipeworx and ask_pipeworx_grounded, and among the many Polymarket tools, but descriptions help differentiate them.

Naming Consistency3/5

Naming conventions vary: some use verb_noun (list_parks), others use descriptive phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). While all use snake_case, there is no consistent pattern in prefixes or verb choice.

Tool Count2/5

35 tools is too many for a coherent server. The set appears to bundle multiple unrelated domains (NPS, Pipeworx, Polymarket, memory, subscriptions) into a single server, lacking focused scope.

Completeness3/5

Each domain has notable gaps (e.g., NPS lacks real-time conditions; Pipeworx lacks data ingestion tools; Polymarket lacks order placement). Some sub-areas (memory, subscriptions) are complete, but overall the surface is incomplete for the broad range of domains.

Resources