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Ens

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)

TDQS

A4.2/5.0
Behavior4/5

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

Annotations provide idempotentHint=true and destructiveHint=false, and the description adds key behavioral details: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'. No contradictions. Additional context about scoping by identifier is helpful.

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?

Four sentences that are front-loaded with purpose, then usage, then storage details. No fluff—every sentence adds value. Highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (2 required params, no output schema), the description covers purpose, usage, storage scope, persistence details, and sibling tool references. It is complete enough for an agent to use correctly. Minor omission: no mention of overwrite behavior or size limits, but these are implicit from idempotency.

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 clear descriptions for both key and value. The description reinforces the key-value pair concept but does not add substantive new semantics beyond the schema. Baseline of 3 is appropriate.

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 saves data for reuse later, with specific examples like 'resolved ticker, target address, user preference'. It distinguishes from sibling tools recall and forget, making its unique 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'when you discover something worth carrying forward'. It also advises pairing with recall for retrieval and forget for deletion. While it lacks explicit when-not scenarios, the guidance is sufficient for typical use.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly defined, distinct purpose. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by use case (casual vs. high-stakes), and the prediction market tools each cover a specific function (research, edge detection, arbitrage, fill risk, tracking, cross-venue spreads). Detailed descriptions eliminate ambiguity.

Naming Consistency4/5

Tool names predominantly follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity), but a few use noun_noun or adjective_noun forms (e.g., entity_profile, recent_alerts). The naming is generally predictable and readable, with minor deviations from a strict pattern.

Tool Count3/5

The server offers 32 tools, which is above the typical 3-15 range for a well-scoped server. However, the vast domain (financials, prediction markets, news, memory, subscriptions, etc.) justifies the count. It is on the heavy side but still manageable with clear organization.

Completeness5/5

The tool surface is remarkably complete for the apparent domain: exploration (discover_tools, suggest_questions), identifier resolution (resolve_entity), data retrieval (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes, validate_claim), prediction market analysis (full suite), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), and extras (ENS, dependency scan, AI visibility). No obvious gaps exist.

Resources