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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. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: output is category-bucketed examples tied to the live catalog, and behavior varies based on whether topic is provided. This goes beyond the annotations without contradicting them.

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 dense but every element earns its place: query variants, output structure, category list, argument behavior, and usage timing. It is longer than a minimal description but appropriate for an onboarding tool that needs to teach the agent what it can do.

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 tool with one optional parameter and no output schema, the description fully covers purpose, output characteristics, argument semantics, and when to use it. The agent can correctly select and invoke this tool based on the description alone.

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?

Schema coverage is 100% and the one optional parameter is already well-described. The description repeats the topic examples but adds no new constraints or syntax details, so the baseline of 3 applies.

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 this is the onboarding entry point that returns categorized example questions with the exact tool and argument shape that answers them. It distinguishes itself from siblings by prescribing to 'Use this FIRST' when the agent does not know Pipeworx capabilities.

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?

Provides explicit when-to-use 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') and explains no-arg vs topic-focused invocation. It stops short of naming alternatives or explicit when-not-to-use conditions, but the 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

A3.7/5.0
Disambiguation2/5

The set has several overlapping tool clusters. ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all target opportunity-finding/fill-checking on prediction markets. While individual descriptions are detailed, an agent could easily pick the wrong tool among these near-duplicates.

Naming Consistency2/5

Naming is inconsistent across the surface. The monday_* and polymarket_* prefixes are consistent within their subgroups, and ask_pipeworx_* forms a family, but the rest mix verb_phrase (validate_claim, compare_entities, discover_tools), noun_phrase (entity_profile, recent_changes, suggest_questions), and bare verbs (remember, forget, recall) with no unifying pattern. This makes it hard to predict tool names.

Tool Count2/5

At 36 tools, the surface is overloaded. The Monday.com integration alone only needs 5 tools, and the remaining 31 are a sprawling research/meta-toolkit. Many of these could be consolidated (e.g., ask_pipeworx and ask_pipeworx_beta, or the several polymarket scanners), so the count feels inflated beyond what the server's core purpose requires.

Completeness3/5

The data-research and monitoring side is thorough, covering querying, grounding, comparison, profiling, entity resolution, subscriptions, memory, and feedback. However, the Monday.com integration is incomplete: it offers create/list/get/search for items but no update or delete operations, and no board creation or modification. This leaves the Monday workflow with dead ends.