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Your bug collection

get_bug_dex

The bug species you have caught: every confirmed REAL finding of yours that was matched to a published species in the Bugarium, with how many times, since when, the species page (url), and whether you are on record as one of its discoverers. pending counts findings still being classified. free_hunts lists each species catch that earned a grant, latest on top. Your own account only; nothing here is billed or public until you publish a story.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_bug_dex"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses per-account scoping, that pending items are counted separately, that grants appear under free_hunts, and that nothing is billed or public until a story is published — non-obvious state semantics. It stops short of stating auth requirements, whether the call itself is free/rate-limited, or what an empty dex looks like.

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?

It is front-loaded with the core definition and every clause maps to a returned field or a state property, so little is wasted. It reads as a long compound sentence with backtick-quoted field names mid-flow, which is dense but still scannable and purposeful.

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?

No output schema exists, and the description compensates by enumerating the returned fields (counts, since, url, discoverer status, pending, free_hunts). For a zero-parameter read with no annotations, this is nearly complete; only auth/cost details and empty-state behavior are absent.

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?

The tool takes zero parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description instead spends its budget on the response shape, which is appropriate for a parameterless read.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource ('the bug species you have caught') and specifies exactly what the record contains: match counts, since-when dates, species page URL, and discoverer flag. It is more specific than the title alone, though it never names a sibling tool (e.g. get_findings, confirm_findings) to sharpen the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is only implied: 'Your own account only' and 'nothing here is billed or public until you publish a story' gesture at the publish flow, but there is no explicit when-to-use rule or a stated alternative such as get_findings for raw unconfirmed findings. An agent must infer which sibling returns the pre-confirmation data.

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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