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Dbnomics

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

A4.9/5.0
Behavior5/5

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

The description adds behavioral details beyond annotations: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours,' 'scoped by your identifier,' and linking to recall/forget. Annotations already indicate idempotentHint=true and destructiveHint=false, which the description reinforces without contradiction.

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-loading the core purpose and immediately followed by usage guidance and storage details. Every sentence adds information without redundancy or 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?

Given the tool's simplicity (2 required string params, no output schema), the description covers all essential aspects: purpose, usage context, storage behavior, persistence, and companion tools. No gaps remain for effective use.

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?

The schema covers both parameters with descriptions (100% coverage). The description adds value by providing example key formats ('subject_property, target_ticker') and value types ('findings, addresses, preferences, notes'), which aids in understanding proper usage beyond the schema's brief descriptions.

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 'Save data the agent will need to reuse later' with specific verb 'Save' and resource 'data' (key-value memory). It distinguishes from siblings like 'subscribe' by specifying key-value pair scoped by identifier and cross-session reuse, differentiating it from other tools.

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 explicitly says when to use ('when you discover something worth carrying forward') and provides alternatives: 'Pair with recall to retrieve later, forget to delete.' It also clarifies the benefit ('so you don't have to look it up again'), giving clear context for choosing this tool over others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Dbnomics toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, while bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. The detailed descriptions mitigate some confusion, but an agent must read carefully to avoid selecting the wrong member of these overlapping groups.

Naming Consistency3/5

The set is consistently snake_case and many tools follow verb_noun conventions like list_datasets, get_series, find_series, and validate_claim. However, a large minority are noun-led names such as entity_profile, deep_research, bet_research, pipeworx_feedback, and polymarket_arbitrage, so there is no single predictable naming pattern across the whole server.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for an over-heavy surface, and the set spans DBnomics data, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI-visibility checks. Several near-duplicate meta-tools could be consolidated, and unrelated domains would be better split into separate servers.

Completeness4/5

Core workflows are well covered: DBnomics browse/fetch/search, company resolve/profile/compare/change, prediction-market discovery and fill-risk, and memory/subscription lifecycles are all represented. Minor gaps exist, such as no subscription-update operation and no direct single-dataset detail fetch without listing, but agents can work around them.

Resources