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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 write operation, idempotent, non-destructive. The description adds valuable context: persistence differs for authenticated vs anonymous users (24-hour retention), and memory is scoped by identifier. This goes beyond annotations without contradicting them, though it doesn't specify overwrite behavior for duplicate keys.

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 compact and well-organized: primary action first, then usage guidance, storage details, and companion tools. Every sentence adds distinct value with no redundant filler.

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 two-parameter key-value store with complete schema documentation, the description covers the essential aspects: what is saved, when to use it, storage scope, retention policy, and how to interact with related tools. No output schema is needed for a save operation.

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 descriptions for both key and value. The description reinforces the parameter purpose with examples (resolved ticker, target address) that largely mirror the schema's suggested values, adding minimal new semantic detail.

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 states a specific action and resource: 'Save data the agent will need to reuse later' as a key-value pair. It clearly distinguishes from sibling tools by naming recall and forget for retrieval and deletion, making the purpose 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?

Provides explicit guidance: 'Use when you discover something worth carrying forward' and gives concrete examples like ticker or user preference. It also names the alternatives (recall to retrieve, forget to delete), giving clear when-to-use versus when-not-to-use context.

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

C2.9/5.0
Disambiguation3/5

The 40 tools span Ethereum RPC, Pipeworx data lookup, prediction markets, memory, and subscriptions, creating several overlapping clusters (ask_pipeworx variants, polymarket_edges vs polymarket_arbitrage vs bet_research). Detailed descriptions help, but an agent could still misselect among the deeply related prediction-market tools or the ask_pipeworx family.

Naming Consistency3/5

Snake_case is consistent, but the convention mixes verb-first names (ask_pipeworx, validate_claim, generate_llms_txt) with noun-first names (token_balances, nft_owners, recent_alerts) and RPC-derived names (eth_call, asset_transfers). It is readable but lacks a uniform verb_noun pattern.

Tool Count2/5

40 tools is well over the 25+ threshold for a coherent surface, and the server is named 'Alchemy Eth' while the majority of tools belong to Pipeworx and Polymarket. The count is far too heavy for the apparent Ethereum-focused scope, and would benefit from being split into separate servers.

Completeness3/5

The Ethereum subset is read-heavy (transfers, tokens, NFTs) but the generic eth_call passthrough covers arbitrary RPC methods, partially filling gaps. The Pipeworx side is fairly complete with query, research, grounding, subscriptions, and memory. Overall, the mixed domain makes the full surface feel incomplete with no unified lifecycle.