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Food Feeds

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context: returns live-catalog-derived content with exact tool/argument shapes, and the parameter behavior. No contradictions.

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?

The description is fairly long but front-loaded with example queries and key info. Each sentence adds important detail, though some trimming of example enumeration could improve efficiency.

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?

No output schema, so description covers return values ('Returns category-bucketed example questions...'). Also covers parameter behavior and when to use. Complete for onboarding tool.

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%, so baseline is 3. The description adds examples ('finance', 'pharma', 'betting') and clarifies the effect of topic, adding value 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's function with specific verbs: 'the onboarding entry point... Returns category-bucketed example questions... each with the exact tool + argument shape that answers it.' It distinguishes from siblings by focusing on question suggestions and learning meta-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?

Explicit usage guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools...' Also specifies when to pass topic vs no arguments.

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

All Food Feeds toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Many tools have overlapping functionality (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the set includes both food-specific feeds and general data tools without clear separation. Distinguishing between them, especially for an agent, would be difficult.

Naming Consistency2/5

Tool names follow no consistent pattern: some are snake_case (list_feeds, read_feed), others are lowercase with underscores (ai_visibility_check), and many are multi-word without clear structure (polymarket_arbitrage, scan_dependency). This inconsistency makes it hard to predict tool names.

Tool Count1/5

With 34 tools, the count is high, and most tools are unrelated to the server's stated purpose of 'Food Feeds'. The inclusion of general-purpose Pipeworx tools (e.g., deep_research, entity_profile, polymarket tools) makes the set bloated and unfocused.

Completeness2/5

For the food feeds domain, only three tools (list_feeds, read_feed, fetch_feed) are relevant, which is incomplete. The server lacks tools for searching, subscribing, or managing feeds. The presence of many unrelated tools does not compensate for this gap.

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