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Statbank Md

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?

The description discloses persistence behavior (authenticated users persistent, anonymous sessions 24 hours) and scoping ('scoped by your identifier'), adding value beyond annotations. Annotations already declare idempotentHint=true and destructiveHint=false, so the description enriches the safety profile without contradicting it.

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-loaded with purpose, then usage, then key behavioral details. Every sentence adds distinct value, with no redundancy or fluff.

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?

For a simple 2-parameter tool with no output schema, the description covers purpose, usage, persistence, scoping, and relationships to sibling tools. It is complete and self-sufficient, providing all necessary context for an agent to invoke it correctly.

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 input schema covers both parameters fully (key and value, each with descriptions and examples). The description mentions 'key-value pair' and value flexibility but adds little beyond the schema's 100% coverage. Baseline of 3 is appropriate given the schema already provides strong semantics.

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's function with a specific verb ('Save data') and resource ('data the agent will need to reuse later'), and explicitly distinguishes it from siblings by mentioning pairing with 'recall' and 'forget'. The scope ('across this conversation or across sessions') is also clarified.

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 provides clear guidance on when to use the tool ('Use when you discover something worth carrying forward') with concrete examples, and explicitly names alternatives ('Pair with recall to retrieve later, forget to delete'), offering clear usage vs. alternative context.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in scope, and the prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) share similar functions. While some tools are distinct, the boundaries between many are unclear.

Naming Consistency2/5

Names mix product-like identifiers (ask_pipeworx, deep_research), descriptive nouns (entity_profile, subjects), and inconsistent verb forms (query_table, resolve_entity, scan_competitor_ai_presence). No consistent verb_noun pattern is maintained across the set.

Tool Count2/5

With 34 tools, the server is overpopulated relative to its apparent purpose. The name 'Statbank Md' suggests a narrow statistical service, but only 3 tools are Statbank-specific; the rest form a sprawling general-purpose data toolkit. The count is far beyond what the core function needs.

Completeness3/5

For the Statbank subset, the surface is complete (browse subjects, get metadata, query data). As a general data research suite, it covers many domains (SEC, FDA, economics, prediction markets) but lacks execution/trading tools for prediction markets and has no bulk data export or analytics beyond excerpts. Notable gaps exist but many core workflows are covered.

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