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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 declare the tool as non-destructive, idempotent, and non-read-only. The description adds valuable behavioral context beyond this: key-value scoping by identifier, persistence differences between authenticated (persistent) and anonymous (24-hour) sessions, and the ability to span conversations/sessions. This enhances the agent's understanding without contradicting annotations.

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?

Three concise sentences: first states the primary purpose, second gives actionable usage examples, third covers storage details and companion tools. Information is front-loaded, with no redundancy or filler. Each sentence earns its place.

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?

The description is complete for this simple write tool: purpose, usage, scoping, persistence, and relationship to siblings are all covered. With a fully documented schema and no output schema, there is no missing critical information. It adequately equips the agent to select and 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?

The schema covers both parameters with descriptive examples (key and value), so the description need not repeat them. The phrase 'key-value pair scoped by your identifier' adds slight semantic context about the key's scoping, but overall the description offers minimal additive parameter detail. Baseline 3 is appropriate given full schema coverage.

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 opens with a specific verb and resource ('Save data the agent will need to reuse later') and clarifies the key-value store nature. It distinguishes itself from siblings by explicitly pairing with 'recall' and 'forget', which are close alternatives, making its role unmistakable.

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 with concrete examples ('a resolved ticker, a target address, a user preference, a research subject'). It also tells the agent when to use companion tools ('Pair with recall to retrieve later, forget to delete'), effectively covering both use and non-use cases.

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 occupy nearly the same question-answering role: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all accept natural-language factual queries, and ask_pipeworx_beta is explicitly identical to ask_pipeworx today. The polymarket_* family also has overlapping opportunity-detection responsibilities, so an agent can easily select a near-duplicate tool for the same intent.

Naming Consistency4/5

Tool names are uniformly lowercase snake_case and use recognizable domain prefixes like ask_pipeworx_, polymarket_, datalastic_, and pipeworx_, which makes the set easy to group. The main deviation is that some names are verb-first (ask, compare, subscribe) while others are noun phrases (entity_profile, recent_alerts, bet_research), but this is minor and readable.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold for a single server, and many of them are meta-tools or overlapping research/opportunity scanners that could be consolidated. The breadth is justified only partially; several utilities like generate_llms_txt and scan_dependency feel bolted on.

Completeness3/5

As a general research and monitoring toolkit it covers the full life cycle: discover, ask, verify, compare, profile, cite, remember, subscribe, and alert. But the server is named Datalastic yet the maritime surface is thin (only live position and radius lookup, no historical tracking, port calls, or fleet tools), and a few one-off utilities don't fit a coherent domain.