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K4L-EL

pyon-mcp

by K4L-EL

List research reports

list_research

List saved research reports to locate an analysisId or verify existing research before generating a new report.

Instructions

List the user's saved research reports. Use this to find an analysisId for get_research or to check whether relevant research already exists before generating a new report with create_research. Parameters: none - the arguments object may be empty or omitted entirely. Returns a compact list of up to 25 reports with analysisId, title, symbol, and timestamps where available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the return format ('compact list of up to 25 reports with analysisId, title, symbol, and timestamps') and parameter expectations ('arguments object may be empty or omitted'). However, it does not explicitly state the operation has no side effects (read-only), though 'List' strongly implies it. A explicit note would push this to 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a parameter note, front-loaded with the core action and resource. Every phrase provides actionable information (purpose, usage, return details) with no redundancy or filler.

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?

For a parameterless tool with no output schema, the description sufficiently covers return values (fields and limit), use cases, and relationship to sibling tools. It addresses all critical aspects an agent needs to invoke and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has zero properties, so baseline is 4. The description adds valuable invocation guidance: 'Parameters: none - the arguments object may be empty or omitted entirely.' This clarifies that the agent can omit the arguments object altogether, which is not evident from the schema alone. Explicitly handling the absence of parameters earns a 5.

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 specifies the action ('List') and the resource ('the user's saved research reports'), distinguishing it from siblings like list_strategies (strategies) and get_research (specific report retrieval). The phrase 'saved research reports' unambiguously identifies the scope.

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 when to use: 'to find an analysisId for get_research' and 'to check whether relevant research already exists before generating a new report with create_research'. This names sibling tools and provides concrete decision context, fully satisfying the dimension.

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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