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jean — Molecular Biology & Genomics

list_traces

Read-only

List the Sanger trace files in this project (.ab1 / .abi / .scf / .ztr) — the list a batch runs over. Wire its names into a trace reader's For-each so every read in the project is processed in one pipeline, instead of one node per read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_filesNoThe project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
_subjectNoThe record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYestheir project file ids, in the same order (unambiguous where two names match)
countYeshow many traces that is
namesYesthe trace file names, in project order — the list to scatter a trace reader over

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful context beyond that: the output's `names` key is the integration point and the scope is all traces 'in this project', which an agent cannot infer from annotations alone.

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?

Two tightly written sentences, front-loaded with the action and file scope, followed by the concrete wiring instruction. No filler, every clause 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?

With an output schema present, return values need not be re-explained, and the annotations carry the safety profile. The description supplies the scope and the downstream integration pattern, leaving only minor input-parameter detail to the schema.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description references 'this project' (mapping loosely to _files) but adds no syntax or meaning for the two inputs beyond what the schema already documents; `names` it mentions is an output field, not an input.

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?

States a specific verb and resource ('List the Sanger trace files in this project') and enumerates the exact file extensions covered (.ab1/.abi/.scf/.ztr), so an agent knows precisely what it returns and can distinguish it from siblings like load_trace or draw_trace that consume a single trace.

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?

Gives clear usage context: it produces 'the list a batch runs over' and instructs wiring its `names` into a trace reader's For-each to process every read in one pipeline rather than one node per read. This is a strong when-to-use statement, though it does not name an explicit sibling alternative to avoid.

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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