format_sequenceClean, case-fold, DNA↔RNA convert, reverse and line-wrap a sequence.
| Name | Required | Description | Default |
|---|---|---|---|
| width | No | Line-wrap width; 0 = single line. | |
| convert | No | DNA→RNA (T→U) or RNA→DNA (U→T). | none |
| reverse | No | Reverse the sequence (no complement). | |
| caseMode | No | keep | |
| sequence | Yes | Nucleotide sequence (raw or FASTA; IUPAC accepted). | |
| stripNonLetters | No | Remove digits, spaces and gaps (keep letters only). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true, so the description does not need to cover safety. It adds value by listing operations (cleaning, conversion, etc.) but does not disclose additional behavioral traits beyond what the schema provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key operations. It contains no wasted words, but could be slightly more structured for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters, no output schema, and good annotations, the description adequately summarizes the tool's capabilities but does not mention the output format or any edge cases. It is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 83% schema description coverage, the schema already documents most parameters. The description summarizes the operations (clean, convert, reverse) that map to parameters, but does not add meaning beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Clean, case-fold, DNA↔RNA convert, reverse and line-wrap a sequence.' It specifies the verb (format) and the resource (sequence), and lists distinct operations that differentiate it from siblings like reverse_complement or translate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it. No exclusions or comparisons to sibling tools are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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'.
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.
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.