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Glama

Data Delaware

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

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, which are consistent. The description adds behavioral details like scoping and retention (persistent for authenticated users, 24h for anonymous), but does not clarify overwrite behavior upon repeated same key.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is relatively concise and front-loaded with purpose, then guidance, then storage details. Every sentence adds value, though could be slightly tighter.

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?

For a simple write tool with no output schema, the description covers purpose, usage, scoping, and retention adequately. It is complete for the tool's complexity.

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%, and description adds concrete examples for key and value, enhancing understanding beyond the schema.

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, specifying key-value pairs scoped by identifier. It distinguishes from siblings recall and forget, which are mentioned explicitly.

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?

Explicit guidance on when to use the tool (discover something worth carrying forward) and pairing with recall/forget provides clear context and alternatives.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route queries to the same data sources with only subtle differences. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all analyze prediction markets, making it hard for an agent to pick the right one without deep reading of descriptions.

Naming Consistency3/5

Tool names follow a mix of patterns: some are verb_noun (generate_llms_txt, list_subscriptions), some noun_verb (ai_visibility_check, bet_research), and some are just nouns (datasets, metadata). The ask_pipeworx family has consistent prefixes but suffixes vary. Overall readable but inconsistent.

Tool Count2/5

34 tools is excessive for a coherent server. The server tries to be a Swiss Army knife covering data lookup, prediction markets, Delaware open data, memory, subscriptions, and misc tools like generate_llms_txt and scan_dependency. Many tools feel tacked on, and the count makes it unwieldy for an agent to navigate.

Completeness3/5

The server covers multiple domains thoroughly (data query via Pipeworx variants, prediction markets with arbitrage and edges, Delaware open data, memory, subscriptions). However, there are gaps: no tool for managing custom pipelines or for updating data. For the broad scope, it is decent but not fully comprehensive.

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