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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

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnly=false and idempotentHint=true. The description adds valuable behavioral context beyond annotations: persistence semantics (authenticated vs anonymous sessions, 24-hour retention), scoping by identifier, and the key-value storage model. It does not contradict any annotation.

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 three sentences, front-loaded with the core purpose, and every sentence provides useful context (when to use, storage model, persistence, companion tools) without redundancy or fluff.

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 memory storage tool with two parameters, no output schema, and good annotations, the description covers all essential aspects: purpose, usage triggers, storage format, persistence, scope, and integration with recall/forget. No significant gaps remain.

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%, so both key and value are well-documented in the schema. The description adds illustrative examples (e.g., 'subject_property', 'target_ticker') and clarifies value can be 'any text,' but this is marginal enrichment over the schema, not essential meaning.

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 action: 'Save data the agent will need to reuse later,' with a specific resource (key-value pair scoped by your identifier). It distinguishes from siblings by explicitly pairing with recall and forget, and lists concrete examples of what to store (ticker, address, preference).

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?

Provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names alternatives: 'Pair with recall to retrieve later, forget to delete,' giving clear context versus sibling tools.

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions, while the polymarket_edges, polymarket_arbitrage, and related tools blur edge-detection boundaries. The server's Bitstamp identity also clashes with the bulk of tools being unrelated data-research, making selection harder.

Naming Consistency3/5

Tool names are all lowercase snake_case, which is consistent formatting, but no coherent verb_noun pattern emerges. Some are verb-first (ask_pipeworx, compare_entities, discover_tools) while others are noun-first or resource-based (ticker_hour, order_book, polymarket_edges), and the naming style differs across the two major domains.

Tool Count2/5

At 38 tools, the set is well over the 15-tool threshold for a focused server, and the majority of tools are unrelated to the server's Bitstamp name. The count feels bloated and scattershot—it would be better split into separate data-research and exchange servers.

Completeness3/5

The data-research and question-answering surface is broadly covered, with meta-tools and validation. However, the Bitstamp exchange half is incomplete: it only provides public market data (ticker, order book, trades, OHLC) with no trading, account, or private-data operations, an obvious gap given the server's name.