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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. Added

TDQS

A4.9/5.0
Behavior5/5

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

The annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond that: scoping by user identifier, persistence differences between authenticated users and anonymous sessions, and the ability to delete via 'forget'. This exceeds what annotations alone 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 well-structured and front-loaded, opening with the core purpose and then providing usage triggers, storage details, and companion tools. Every sentence adds value, with no redundancy or filler. Despite being moderately long, it remains concise given the information covered.

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 key-value store with two required parameters and no output schema, the description is complete. It explains the tool's purpose, when to use it, storage model, persistence scoping, and related tools. No critical information is missing for an agent to select and invoke it correctly.

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?

Schema coverage is 100% for both parameters, so a baseline of 3 applies. The description adds meaningful examples beyond the schema by illustrating typical keys ('a resolved ticker, a target address, a user preference') and value types ('findings, addresses, preferences, notes'), enriching parameter understanding without replacing 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 with a specific verb ('Save') and resource ('data the agent will need to reuse later'). It distinguishes from siblings by explicitly naming 'recall' and 'forget' as companion tools, making the tool's role in the memory workflow clear.

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 provides explicit use cases ('Use when you discover something worth carrying forward') with concrete examples (resolved ticker, target address, user preference, research subject). It also names alternatives/companion tools ('Pair with recall to retrieve later, forget to delete'), satisfying the highest bar for usage 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
Disambiguation2/5

The toolset contains several heavily overlapping families: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and polymarket_edges, polymarket_arbitrage, and bet_research cover adjacent purposes. Even though descriptions are detailed and attempt to differentiate, an agent can easily route a query to the wrong member of a cluster.

Naming Consistency2/5

Most names are readable snake_case, but the set does not follow a single convention: it mixes verb-first names (get_flood_forecast, subscribe), noun-phrase names (entity_profile, recent_changes, pipeworx_feedback), and product-prefixed families (polymarket_*). The verb style is also inconsistent across ask, get, list, scan, validate, generate, and discover, so names don't reliably predict what a tool does.

Tool Count2/5

33 tools is well above the 25-tool boundary for a coherent set, and the server's nominal 'flood' scope accounts for only two of them. The rest belong to unrelated domains like general data lookup, prediction markets, memory, and subscriptions, making the set feel like several MCP servers merged together.

Completeness3/5

For the broad data-agent scope, coverage is fairly strong: querying, entity resolution, memory lifecycle, subscription lifecycle, and prediction-market analysis all have their major operations represented. However, the flood domain implied by the server name is thin—only forecast and river discharge, with no historical series, flood-specific alerting, or location-focused risk tools—so the surface is not clearly complete for any single stated purpose.