id_map_pollCheck a UniProt id-mapping job submitted via id_map_submit. Returns {status, ready:false} while still running; once FINISHED, also returns the mapped ids (normalized regardless of which target database was requested) and any ids that failed to map.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, indicating safe, idempotent polling. The description adds value by detailing the response format and a key behavioral trait: normalization of mapped ids regardless of target database. No contradictions with annotations.
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?
Two efficient, front-loaded sentences. The first states purpose, the second details response lifecycle. No unnecessary words. Perfectly concise for the complexity.
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?
For a simple polling tool with one parameter and sufficient annotations, the description is complete. It explains the return value structure and a notable behavior (normalization). No output schema exists, but the description adequately covers what is returned.
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 schema description coverage at 0%, the description must clarify the parameter. It mentions 'job submitted via id_map_submit', implying the jobId comes from that submission. However, it does not specify format or constraints beyond what the schema provides (type string, required). It adds marginal meaning.
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 purpose: checking a UniProt id-mapping job submitted via id_map_submit. It specifies the behavior (returns status while running, mapped ids and failed ids when finished), and distinguishes it from the submission tool id_map_submit. The verb 'Check' and resource are explicit.
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 explicitly ties usage to a prior id_map_submit call, providing clear context. It doesn't explicitly state when not to use the tool, but the complementary sibling relationship is evident. This is sufficient guidance for an AI agent.
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.