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Biosamples

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

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value beyond annotations by explaining that the tool returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly', which informs behavior.

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 front-loaded with purpose and uses two main sentences plus a list of domains. It is efficient but could be slightly more concise by trimming domain list length.

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 fully explains what is returned (tool names, descriptions, schemas). It covers the input parameters and provides usage examples. For a tool with moderate complexity (6 params, aliases), this is complete.

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 context by stating that the query accepts natural language, lists example queries, and mentions aliases (though schema already documents them). This provides meaningful extra guidance.

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 'Find tools by describing the data or task' and enumerates specific domains (SEC filings, FDA drugs, etc.), distinguishing it from sibling tools like search_samples or deep_research by emphasizing that it returns tool definitions ready to call.

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?

The description explicitly says 'Call this FIRST when you have many tools available and want to see the option set', providing strong usage context. However, it does not explicitly state when not to use this tool or name specific alternatives.

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

All Biosamples toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants (beta currently identical), and the set includes five-plus prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) with fuzzy boundaries. Long descriptions help, but an agent selecting among them would frequently struggle to pick the right one.

Naming Consistency3/5

Names are uniformly lowercase snake_case, but the pattern is inconsistent: verb_first names (search_samples, validate_claim, resolve_entity) mix with noun-style names (entity_profile, pipeworx_trending, polymarket_arbitrage) and bare imperatives (remember, recall, forget, subscribe). It is readable, but there is no predictable verb_noun convention across the set.

Tool Count2/5

33 tools is well over the coherent range, and the count is especially inflated because the server is named Biosamples yet only two tools (search_samples, get_sample) actually belong to that domain. The remaining 31 tools are an unrelated mix of Pipeworx research, prediction-market, memory, and subscription utilities, including redundant variants.

Completeness2/5

For the stated Biosamples purpose, only search and retrieve exist—no submission, update, or batch operations—so the domain surface is a read-only fragment. For the broader accidental scope of the other tools, the set is a grab bag with no coherent lifecycle, leaving significant gaps regardless of which domain is considered primary.

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