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Openreview

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate non-destructive, read-only, idempotent behavior. The description adds context: returns sample questions, drawn from live catalog, with tool-argument shapes, enhancing transparency beyond 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?

Well-structured with trigger phrases first, then explanation of output, then usage. Somewhat lengthy but justified by the need to convey comprehensive onboarding information.

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, the description adequately explains return value structure (category-bucketed examples with tool shapes). Parameter count is low, and annotations cover safety. All necessary context is provided.

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 one optional parameter. The description adds value by listing example values (finance, pharma, etc.) and explaining behavior when omitted, going beyond the schema's basic 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?

The description clearly states it is the onboarding entry point returning category-bucketed example questions. It lists specific trigger phrases like 'What can I ask Pipeworx?' and distinguishes itself from siblings by being the first tool to use when unfamiliar with Pipeworx.

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 says 'Use this FIRST' and provides clear guidance on when to omit topic for full spread or pass a specific topic. It also mentions alternatives for learning meta-tools.

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

A3.5/5.0
Disambiguation2/5

The tool set includes both OpenReview-specific tools (e.g., get_paper, list_submissions) and a large number of unrelated tools for Pipeworx, Polymarket, and SEC filings. While individual descriptions are clear, the mix of domains creates confusion about which tools to use for a given task, leading to potential misselection.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use underscore_case (ai_visibility_check, generate_llms_txt), some are verb_noun (get_paper, list_venues), and others use descriptive phrases (ask_pipeworx_grounded, polymarket_arbitrage). The lack of a unified naming scheme makes the tool set feel disjointed.

Tool Count2/5

With 37 tools, the count is too high for a server supposedly focused on OpenReview. Only about 6 tools are directly related to OpenReview; the rest are for unrelated domains like prediction markets, data retrieval, and memory management. The scope is unclear and overloaded.

Completeness2/5

For the OpenReview domain, the tool set covers basic retrieval (get_paper, search_notes, list_submissions) but lacks operations like creating or updating notes, which are common in a review platform. The inclusion of many non-OpenReview tools does not compensate for these gaps, leaving the surface incomplete for its stated purpose.

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