Skip to main content
Glama

Unicef

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds context about return content (categories with tool shapes) and parameter effect, but no additional behavioral disclosures beyond that.

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 relatively long but well-front-loaded with example queries. It is structured and each sentence adds value, though slight verbosity prevents a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description covers what the tool returns, how to invoke it, and when to use it. With annotations, it is largely complete for a discovery 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% and already describes the topic parameter. The description adds value by clarifying omission gives full spread and providing concrete topic examples.

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 the tool returns category-bucketed example questions with exact tool+argument shapes. It is an onboarding entry point, distinct from siblings that perform other tasks.

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?

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains when to omit args vs. pass a topic.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Unicef toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

Several tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve 'find/query Pipeworx data' with blurry boundaries, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) have overlapping purposes. An agent could easily pick the wrong one without reading every description.

Naming Consistency3/5

Most tools follow snake_case verb_noun patterns (list_dataflows, get_data, compare_entities, resolve_entity), and families share prefixes (pipeworx_*, polymarket_*, ask_pipeworx_*). However, the server is named 'Unicef' while almost all tool names reference Pipeworx/Polymarket, and verb choices vary widely, so the overall set lacks a unified naming story.

Tool Count1/5

34 tools is already on the high side, but the real problem is scope: only 3 tools (list_dataflows, dataflow_structure, get_data) relate to the server's stated UNICEF purpose, while the other 31 are an unrelated grab bag of Pipeworx research, prediction-market betting, memory utilities, npm scanning, and llms.txt generation. This is a severe mismatch between count and purpose.

Completeness2/5

For the UNICEF domain implied by the server name, the surface is minimal: browse, structure, and fetch data cover read-only access but nothing else, and the overwhelming majority of tools are off-domain. If the inferred domain is instead 'Pipeworx + prediction markets', coverage is broad, but then the server name is fundamentally misleading and the UNICEF subset is an incomplete afterthought.

Resources