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Datalastic

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

A4.5/5.0
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

A3.9/5.0
Disambiguation2/5

Several tool clusters have genuinely fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grouned, and deep_research all route to the same 5,767 tools and differ only by use-case nuance, while polymarket_edges, polymarket_arbitrage, and bet_research all surface trading opportunities. scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and entity_profile, recent_changes, and compare_entities pull overlapping company data. The descriptions are detailed, but an agent can easily select the wrong tool in these clusters.

Naming Consistency4/5

All tools use snake_case and family prefixes are consistent (polymarket_*, pipeworx, datalastic_*, scan_*, ask_*), making the set predictable and readable. The main deviation is verb placement — verb-first (list_subscriptions, resolve_entity, search_within) vs noun-first (entiy_profile, recent_alerts, bet_research) — and prefix position varies between ask_pipeworx and pipeworx_feedback, but these are minor.

Tool Count2/5

33 tools exceeds the heavy threshold, and the count is padded by redundancy: four ask_pipeworx variants that are near-identical, six polymarket tools with overlapping scans, and wrapper tools like scan_competitor_ai_presence that just call ai_visibility_check. The server name suggests maritime focus but only two tools serve that domain, while the rest span a sprawling data-research, prediction-market, AI-visibility, and npm-scanning surface. Consolidating the ask_pipeworx family into one router with a mode parameter and merging wrappers would trim the set to roughly 20 tools without losing capability.

Completeness4/5

The core data-research lifecycle is thoroughly covered: resolve_entity feeds entity_profile, compare_entities, recent_changes, validate_claim, and deep_research, and the prediction-market workflow includes discovery, edge detection, fill-risk validation, and cross-venue analysis. Subscriptions, memory, and feedback are well supported. Minor gaps exist — the datalastic maritime piece has only live position lookups (no history or fleet tools), and one-offs like generate_llms_txt and scan_dependency feel unrelated — but there are no critical dead ends.

Resources