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Glama

Sec Regulations

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

A4.4/5.0
Behavior4/5

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

Annotations already cover readOnly, openWorld, idempotent, not destructive. Description adds that it returns example questions with tool shapes from a live catalog, and that it's the onboarding entry point.

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 front-loaded with alternative phrasings and then explains return structure and parameter usage. Could be more concise but remains effective with minimal 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?

Tool has 1 optional param and no output schema; description covers purpose, usage, return value, and parameter behavior completely for the intended onboarding use case.

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. Description adds context about the `topic` parameter's possible values and the effect of omitting it, effectively explaining the single parameter.

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's an onboarding entry point that returns categorized example questions with tool/argument shapes. Distinguishes itself from siblings by being the first tool to use when unsure.

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 to use FIRST when you don't know what Pipeworx can do, and provides guidance on calling with no arguments or with a topic. Does not contrast with specific siblings but implies usage context.

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 tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, polymarket_arbitrage and polymarket_edges both scan for betting opportunities, and ai_visibility_check overlaps with scan_competitor_ai_presence. The long descriptions help, but an agent must read carefully to distinguish the meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) from each other.

Naming Consistency4/5

The overwhelming majority use lowercase snake_case with a verb-first or domain-prefixed noun pattern (ask_pipeworx, sec_regulations_search, polymarket_edges). Minor deviations like entity_profile, recent_alerts, and ai_visibility_check are noun-first, and sec_regulation vs sec_regulations_search is slightly inconsistent, but the overall style is predictable.

Tool Count2/5

At 33 tools this exceeds the 25-tool threshold for 'too many', and most are unrelated to the server's stated name 'Sec Regulations'—only sec_regulation and sec_regulations_search fit that label. While each tool has a defined purpose, the set feels like several distinct servers (research, betting, memory, subscriptions) crammed into one.

Completeness3/5

For a general data-research platform, the surface is broad: Q&A routers, entity resolution/profiles, comparisons, validation, memory, subscriptions, and domain-specific tools. But the SEC-regulation focus implied by the server name is severely under-served, and there's no direct tool to fetch a raw document by citation/URI outside of the ask_pipeworx router. The Polymarket cluster is over-built relative to other data domains.

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