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Sanger Trace Parser

parse_sanger_trace
Read-onlyIdempotent

Decode a Sanger ABIF (.ab1 / .abi) chromatogram: base calls, per-base quality, the four dye-channel traces, peak locations, and the run's own labels (sample name, well, plate, instrument, run start).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNameNoOptional original file name (echoed back).
fileBase64YesThe binary ABIF (.ab1 / .abi) trace file, base64-encoded.
includeTracesNoInclude the four raw dye-channel arrays and peakLocations. They are 93% of the response — a 900-base read is 122 kB with them and 8 kB without (measured) — and they are only useful for DRAWING the chromatogram. Everything you would reason about (base calls, quality, the run's labels) is returned either way, so leave this off unless you are rendering.

TDQS

A4/5.0
Behavior4/5

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

Annotations already mark the tool read-only and idempotent, and the description adds a clear enumeration of returned data categories. The includeTraces parameter description meaningfully discloses that traces make up 93% of the response and are only useful for drawing the chromatogram, which is behavioral context beyond the annotations. It stops short of a 5 by not addressing invalid-file or error behavior.

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?

A single front-loaded sentence uses a colon-delimited list to convey the full scope with zero filler. Every phrase adds information, and it does not waste words repeating the tool name or title.

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?

There is no output schema, but the description enumerates the main return categories: base calls, quality, traces, peaks, and run metadata. The includeTraces size/cost detail closes the key practical gap. It would be a 5 with explicit output shapes or handling notes for corrupt input, but as it stands it is nearly complete for invocation.

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 input schema already carries the full parameter meanings. The main description adds only indirect context by listing outputs such as 'the four dye-channel traces' and 'peak locations,' which map to includeTraces but do not substantially explain the parameters beyond what the schema provides.

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 verb 'Decode' plus the explicit resource 'Sanger ABIF (.ab1 / .abi) chromatogram' and a precise list of returned artifacts makes the tool's function unambiguous. The focus on raw trace extraction differentiates it from analytical sibling tools such as sanger_indel_spectrum and sanger_vs_reference even without naming them.

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?

The description implies use when raw base calls, quality, and traces are needed, but it never states when not to use this tool or recommends alternatives. The includeTraces parameter gives excellent guidance for that switch, but tool-selection guidance among the Sanger siblings is left to inference.

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

A3.6/5.0
Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

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