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Elife

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

A4.7/5.0
Behavior5/5

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

The description reveals key behavioral traits beyond annotations: returns top-N most relevant tools with 'names, descriptions, and full input schemas (with curated examples)' and that 'each result is ready to call directly, no second schema lookup needed.' This aligns with readOnlyHint and idempotentHint, and adds no contradiction.

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 fairly concise but packed with information. It front-loads the core purpose and use cases. Slightly longer than ideal due to extensive examples, but every sentence earns its place.

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 6 parameters and no output schema, the description adequately explains what the tool returns: top-N tools with schemas and examples. It covers the input, behavior, and output sufficiently, leaving no critical gaps for the agent.

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%, but the description adds value by explaining that 'query' accepts natural language and lists aliases (task, q, description, search). It also specifies default and max for limit, providing context beyond the schema alone.

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's purpose: 'Find tools by describing the data or task.' It lists numerous specific use cases (SEC filings, FDA drugs, etc.) and distinguishes from sibling tools by focusing on discovery and browsing rather than direct execution.

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?

Provides explicit when-to-use guidance: 'Call this FIRST when you have many tools available and want to see the option set.' Also specifies use cases like browsing, searching, and discovering tools, leaving no ambiguity about when to invoke this tool.

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

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are differentiated by reliability mode; entity_profile vs compare_entities serve single vs multi-entity; and memorization, subscription, and search tools occupy separate operational niches. No two tools could be easily confused.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun phrases (polymarket_arbitrage, ai_visibility_check), and some mix verb+noun with underscores inconsistently (generate_llms_txt, scan_competitor_ai_presence). This lack of uniformity makes the surface harder to navigate.

Tool Count3/5

34 tools is borderline high. The server spans multiple domains (data query, prediction markets, eLife, memory, subscriptions), and each domain gets several tools, making the overall surface feel bloated. While individual tools are justified, the total count strains discoverability and hints at scope creep.

Completeness2/5

The tool set is incomplete relative to its stated breadth. For a server named 'Elife', only 3 tools actually serve eLife; the rest are dominated by Pipeworx and Polymarket. Within the query/data domain, coverage is deep but lacks write/modify tools. Prediction market analysis lacks execution tools (no order placement). This leaves clear gaps for agents that need to act on the data.

Resources