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Va Museum

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

TDQS

A4.6/5.0
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 value by explaining that results include full schemas and examples and are ready to call directly, giving the agent a clear understanding of the tool's behavior beyond the 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?

The description is relatively long but efficiently packs many useful elements: purpose, usage guidelines, return behavior, and examples. It is front-loaded with the core function. Could be slightly more concise, but every sentence adds value.

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 what is returned ('names, descriptions, and full input schemas (with curated examples)'). It covers when to use, parameter purpose, and return format, making it complete for the agent to understand and invoke the 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%, so baseline is 3. The description adds meaning by explaining the intent of the query parameter ('Natural language description of what you want to do') and listing aliases, though it does not mention the 'limit' parameter explicitly. Provides 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 starts with 'Find tools by describing the data or task,' clearly stating the verb (find) and resource (tools). It distinguishes itself from sibling tools by positioning itself as the first call for exploring available tools.

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 when you need to browse, search, look up, or discover what tools exist for:' followed by a list of domains. Also instructs 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Provides clear context and exclusions.

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

All Va Museum toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap between ask_pipeworx and ask_pipeworx_grounded (same underlying data query, different answer modes), and multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges) can confuse agents about which to use for a given betting query. Memory tools (remember/recall/forget) are clear, but the mix of museum, financial, and prediction market tools under one server increases ambiguity.

Naming Consistency3/5

All tool names use snake_case consistently, but the naming pattern is inconsistent: some start with a verb (search_objects, list_subscriptions, remember) while others start with a noun or modifier (ai_visibility_check, entity_profile, polymarket_arbitrage). The 'ask_' prefix is used twice, but overall there is no single predictable convention like verb_noun across the set.

Tool Count3/5

29 tools is high but not excessive for a general-purpose data server. However, the server is named 'Va Museum' which implies a narrow domain, making the count seem bloated. The set includes many tools unrelated to a museum (e.g., prediction markets, SEC filings), so the count is appropriate only if the server's actual scope is broad and multi-domain.

Completeness2/5

For a museum-focused server, the tool surface is severely incomplete, with only two museum-specific tools (search_objects, get_object) out of 29. Even as a general-purpose server, it lacks tools for common operations like updating or deleting resources, and the coverage of domains (e.g., no tool for creating or managing user data) feels ad hoc rather than systematically complete.

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