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Censtatd Hk

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

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by explaining the return structure (category-bucketed example questions with tool shapes) and call behavior (no args for full spread, topic to focus). No contradiction. Could mention idempotency but not required.

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 well-structured: starts with common questions, then describes output and usage. It is informative without being verbose, though slightly longer than minimal. Every sentence adds value.

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 an onboarding tool with no output schema, the description fully explains what the tool returns (category-bucketed examples with tool shapes), how to invoke it, and its role. It is complete given the tool's simplicity and the annotations provided.

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% with one optional parameter described. Description adds context: examples of valid topic values and the effect of omitting vs. providing a topic. This goes beyond the schema's minimal description.

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 uses specific verbs and resources: 'onboarding entry point' that 'returns category-bucketed example questions' with exact tool and argument shape. It clearly distinguishes from siblings like ask_pipeworx and discover_tools by stating it answers 'what can you do?' and provides examples.

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: 'first when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Also covers optional topic filtering. No explicit when-not-to-use or alternatives, but context is clear enough.

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

A4/5.0
Disambiguation4/5

Most tools target a distinct action or resource, and the long routing descriptions make choices like ask_pipeworx vs ask_pipeworx_grounded vs deep_research clear. The main weak spots are ask_pipeworx_beta being currently identical to ask_pipeworx and the six overlapping Polymarket tools, but each has a discernible workflow.

Naming Consistency3/5

Naming has internally consistent subfamilies such as censtatd_*, ask_pipeworx*, and polymarket_*, but overall it mixes verb-first names (get_table, validate_claim, subscribe), noun-first names (entity_profile, bet_research, pipeworx_feedback), and bare imperatives (remember, forget, recall). The set is readable but does not follow one convention.

Tool Count2/5

35 tools exceeds the 25+ threshold and feels heavy, especially since many tools (generate_llms_txt, scan_dependency, pipeworx_feedback, pipeworx_trending) are unrelated to the HK Census core implied by the server name. The broad Pipeworx scope explains the width, but the surface is still large for an agent to navigate efficiently.

Completeness4/5

For a read-only research/data-access gateway, coverage is strong: generic lookup, grounded verification, deep research, entity profile/compare/change, entity resolution, memory, and subscription lifecycle are all represented. Minor gaps exist, such as no subscription update, no explicit bulk/export path, and fewer HK C&SD convenience wrappers, but agents can work around them.

Resources