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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint, etc.), the description reveals that the tool returns 'category-bucketed example questions' with 'exact tool + argument shape', and that it draws from a 'live catalog of thousands of tools'. This adds behavioral context not present in annotations.

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 somewhat lengthy but well-structured: starts with example queries, then explains the output, then usage. Every sentence adds value, but the list of categories could be slightly trimmed without losing clarity. Still, it's appropriately detailed.

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?

The description covers purpose, usage, parameter behavior, and output format. With one optional parameter and no output schema, the description is fully sufficient for an AI agent to correctly invoke the 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 value by listing the topic values (finance, pharma, etc.) and explaining the behavior when omitted, 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 explicitly states the tool is the onboarding entry point for Pipeworx, listing synonymous questions like 'What can I ask Pipeworx?' and 'what is Pipeworx good for?'. It clearly distinguishes from siblings by focusing on getting started and learning what questions to ask, contrasting with tools like 'discover_tools' or 'deep_research'.

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 explicit usage instructions: 'Call with no arguments for the full spread, or pass `topic` to focus.' It also advises 'Use this FIRST when you do not yet know what Pipeworx can do for you', offering clear when-to-use guidance.

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

Several near-overlapping query tools exist: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and ask_pipeworx_beta is currently described as identical to ask_pipeworx. The Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) also heavily overlap and rely on lengthy descriptions to keep them apart.

Naming Consistency3/5

The set is uniformly snake_case and generally readable, but conventions are mixed: verb_noun (resolve_entity, suggest_questions), noun_verb (bet_research, ai_visibility_check), noun_adj (pipeworx_trending, recent_changes), and bare verbs (size, forget, recall) all appear. Variant suffixes like ask_pipeworx_beta and ask_pipeworx_grounded add further unpredictability.

Tool Count2/5

32 tools exceeds the 'too many' threshold and spans unrelated domains: package size, general data research, prediction markets, memory, and subscription management. The set would feel more coherent at roughly half the count, with several query and Polymarket tools consolidated.

Completeness3/5

For the dominant data-research purpose, the surface is reasonably complete: querying, grounded verification, deep research, entity resolution, entity profiles, comparisons, claim validation, and discovery are all covered. However, the server's stated identity ('Packagephobia') is nearly absent—only `size` and `scan_dependency` address package sizing—so the namesake domain is thin while unrelated domains are overbuilt.

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