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Cta

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

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. Description adds behavioral context by describing the output format (category-bucketed examples with tool shapes) and that it's an exploratory onboarding tool, complementing annotations without contradiction.

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 dense and front-loaded with query variants, then explains output and usage. Every sentence adds value, but it could be more structured (e.g., bullet points) for quicker scanning. Still efficient with no waste.

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?

Given no output schema, description adequately explains what the tool returns (category-bucketed example questions with tool+argument shapes). It covers usage, parameter semantics, and is contextualized as an onboarding tool. Complete for its purpose.

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 description for topic. Description adds value by listing example focus areas ('finance', 'pharma', etc.) and explaining that omitting topic gives a cross-category spread. This goes beyond the schema's 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?

Description clearly states it returns category-bucketed example questions with exact tool+argument shapes. It distinguishes itself from siblings like ask_pipeworx by being the onboarding entry point and providing example queries rather than answering directly.

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 says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to pass topic vs omit. Does not explicitly state when not to use, but context is clear.

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

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all accept natural-language factual questions, and ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx. The entity tools (entity_profile, compare_entities, recent_changes, resolve_entity) and the many Polymarket tools also blur together, making misselection likely.

Naming Consistency3/5

Most names are snake_case and readable, with recognizable prefixes like cta_, polymarket_, and ask_pipeworx_. However, conventions are mixed: bare verbs (remember, forget, subscribe), noun-style phrases (entity_profile, bet_research), and variants like ai_visibility_check vs scan_competitor_ai_presence prevent a single predictable pattern.

Tool Count2/5

35 tools is above the comfortable range, and the count is especially mismatched for a server named 'Cta': only 4 tools actually concern Chicago transit, while the other 31 form a general-purpose research, prediction-market, and memory suite. The set feels like multiple unrelated servers merged together.

Completeness2/5

As a CTA server, the surface is notably incomplete: it has bus/train positions and predictions but lacks alerts, service disruptions, route listings, and station/stop metadata. The broader Pipeworx tools are extensive but appear bolted on, so the overall set has no coherent domain against which completeness can be judged.

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