Gigwa-MCP
Server Quality Checklist
Latest release: v1.7.0
- Disambiguation5/5
Each tool has a clearly distinct purpose, covering import, export, QC, diversity analysis, metadata management, and server utilities. Even similar-sounding tools like 'count_variants' and 'search_variants' are differentiated by output (count vs. list) and behavior.
Naming Consistency5/5Tool names follow a consistent verb_noun pattern, often with prefixes like 'qc_' and 'diversity_' for grouping. Naming is predictable and descriptive, with no mixing of conventions (all snake_case).
Tool Count4/528 tools is slightly above the typical range but justified by the breadth of functionality (import, export, QC, diversity, metadata, utilities). The count feels appropriate for a comprehensive genotype analysis server, though a few could be merged.
Completeness5/5The tool surface covers a complete genotype analysis workflow: data import (VCF, DArTSeq, metadata), export, quality control (call rate, heterozygosity, duplicates, MAF), diversity statistics (PCA, Fst, kinship, tree, structure, core collection), and server management (list, progress, abort, audit). No obvious gaps are present.
Average 4.2/5 across 28 of 28 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 34 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
This server has been verified by its author.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that outputs are written to a CSV, explains the two method options (vcf vs allelematrix), and notes that BrAPI attributes cannot be used for grouping. However, it does not describe error handling, destructive potential, or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a clear opening sentence followed by an enumerated explanation of grouping options. It is well-structured and front-loaded, though could be slightly more compact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 9 parameters, presence of an output schema, and no annotations, the description covers the core functionality thoroughly. It explains group definition, output file, and a limitation. It does not cover all edge cases but is sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant context beyond the schema: it explains the two mutually exclusive grouping approaches (groups_json vs metadata_tsv+group_column), the option to restrict by region, and the effect of max_markers. This aids correct parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes pairwise Weir & Cockerham Fst between groups of samples and writes the output to a CSV. It is specific about the statistical measure and scope, but does not explicitly differentiate from sibling tools like diversity_by_group.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus other diversity tools. The description focuses on how to define groups but does not mention prerequisites, alternatives, or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It does not disclose what happens on validation failure, output format, or side effects, lacking behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise (two sentences) but the second sentence is a bit run-on. It efficiently conveys necessary information without excessive length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description explains parameters and core action, it lacks usage guidelines and behavioral details, making it somewhat incomplete despite presence of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds concrete details beyond the schema, e.g., explaining metadata_type as the ID column name and that tsv_path's first column header must match. This enriches parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's specific verb and resource: 'Validate an individual-metadata file against a Gigwa database without importing.' This distinguishes it from sibling tools like import_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage before import ('without importing') but does not provide explicit guidance on when to use this tool versus alternatives like audit_import_quality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description explains the algorithm and output (core_collection.csv with rank, accession, cumulative coverage, and diversity fraction), but lacks details on potential side effects, permissions, or whether data is modified. It mentions 'writes' files but does not clarify if it is destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with a clear first sentence stating purpose, followed by algorithm and output details. It uses minimal words but is informative, though could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, output schema exists) and no annotations, the description covers the algorithm, inputs, and outputs adequately. It explains the greedy selection method and the files produced, making it largely complete for an informed user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the description adds minimal new info for parameters. It reinforces that 'size' overrides 'fraction' and explains the default fraction, but this is already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Select a core collection' and the resource 'core collection', with a specific algorithm 'Greedy allele-coverage selection (Core-Hunter style)'. It distinguishes from sibling tools like diversity_pca or diversity_fst by focusing on core collection selection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for selecting a core set maximizing allelic diversity, but it does not explicitly mention when to use this tool versus alternatives or provide exclusions. It assumes the user understands the context from the algorithm description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It indicates a read operation ('report') but does not specify side effects, idempotency, or response details. The existence of an output schema (not fully shown) mitigates this somewhat, but the description alone is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the purpose. It is well-structured for quick comprehension, though it is somewhat terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (single parameter, output schema present), the description is largely complete. However, it could note that the progress token is obtained from import functions, though the schema already covers this. The description does not need to explain return values as the output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a single parameter that has a detailed description. The tool description adds the phrase 'given its progress token,' which reinforces but does not substantially add meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('report'), the resource ('current status of a running import'), and the mechanism ('given its progress token'). It effectively distinguishes from sibling tools like 'abort_import' (which aborts) and 'audit_import_quality' (which audits quality).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a progress token is available, but it lacks explicit guidance on when to use this tool versus alternatives, such as when to check status vs. abort or audit. No exclusion criteria or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation without annotations. It states the tool 'list' content, indicating non-destructive behavior. However, no additional behavioral traits (e.g., pagination, latency) are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is concise and front-loaded with the action and target. No redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters and an output schema expected (not shown), the description is mostly complete for a simple listing tool. It could mention that the output contains these three entity types, but the output schema likely covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description adds value by specifying the entities listed (databases, projects, runs) and the 'currently hosted' context, which is sufficient for a param-free tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists databases, projects, and runs on the Gigwa server. The verb 'List' and specific resources distinguish it from sibling tools like 'gigwa_server_info' which provides server info.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. For example, 'gigwa_server_info' could be a sibling with overlapping purpose, but the description does not clarify when to choose one over the other.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses the file format, validation behavior, and that remaining columns become searchable attributes. However, it does not mention side effects like overwriting existing data, permissions needed, or error handling, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two clearly written sentences, front-loaded with purpose, then file format and validation. Every sentence adds essential information with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers input format and validation but does not mention output, error handling, or effects on existing data. Given the presence of an output schema and sibling tools like abort_import, more context on the import process and potential failures would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters have descriptions. The description adds value by explaining that the TSV first column header must match metadata_type and that values must correspond to existing individuals. It also clarifies the validation default and option to skip it, which goes beyond the schema's concise descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'import' and the resource 'individual metadata into an existing Gigwa database'. It specifies the file format (TSV) and structure, making the tool's purpose unambiguous and distinct from similar tools like validate_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for importing metadata TSV files but does not explicitly state when to use this tool over alternatives like validate_metadata. It gives file format requirements but lacks explicit context on prerequisites or scenarios where other tools are more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses outputs (writes CSV, reports pairs and diagonal) and offers a performance hint for large sets. However, it does not mention potential mutation (appears read-only), required permissions, or resource consumption, leaving gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (4 sentences, ~83 words), front-loaded with the core purpose, and every sentence earns its place (formula, output details, performance guidance). No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, schema coverage, and output schema existence, the description covers essential aspects: formula, output files, and performance hint. It does not explain all parameters in depth (schema covers them) but is largely complete for an informed selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, setting baseline at 3. The description adds value by explaining the formula context and explicitly advising when to use method='allelematrix' with max_markers, which enriches the semantic understanding beyond the schema's descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes a VanRaden genomic relationship (kinship) matrix, specifying the formula and outputs (csv, related pairs, diagonal range). This distinguishes it from sibling diversity tools (e.g., diversity_pca, diversity_structure) which address different analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for kinship/relationship analysis but does not explicitly compare to siblings or state when not to use it. It provides guidance on using method='allelematrix' for large sets, but lacks exclusions or alternatives for other diversity analyses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully details the algorithm (PCA, K-means, pseudo-F), output file (structure_clusters.csv), and reporting of chosen K. It also clarifies the in-Python computation, which is a key behavioral trait. However, it does not discuss runtime or memory implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (5 sentences) with a clear front-loaded purpose statement. Every sentence adds value: algorithm overview, parameter range, output details, and implementation context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters (1 required) and an output schema, the description covers the core functionality, output files, and algorithm choice. It does not detail the output schema fields, but the output schema exists. The mention of being consistent with the analysis layer is mildly vague.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 adds context about k_min..k_max evaluation and the use of pseudo-F, but does not significantly enhance the meaning of individual parameters beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs population-structure clustering using PCA and K-means, with an explicit algorithm summary. It distinguishes itself from siblings like diversity_pca (PCA only) and diversity_by_group (maybe predefined groups) by specifying the full clustering pipeline.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for in-Python lightweight clustering without external ADMIXTURE, but does not explicitly state when to use this tool versus siblings. There is no guidance on prerequisites or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It discloses that a CSV is written (non-destructive side effect) and mentions caching for VCF method. However, it does not clarify auth requirements, permission needs, or potential blocking behavior. Also does not explicitly state it is read-only (non-modifying).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is extremely concise: two sentences for purpose and definitions, one sentence for file output, one sentence for optimization hint. No filler, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present, description need not explain return values. It covers statistics, output file, and optimization. However, it does not describe 'dataset means' or explain region/ output_dir parameters, which are left to schema. Slightly incomplete given 5 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value by defining He, Ho, PIC (not in schema) and providing usage hints for method and max_markers (large set optimization). This goes beyond basic parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states tool computes per-marker diversity statistics (MAF, He, Ho, PIC) and dataset means. It lists specific statistics and mentions output file, clearly distinguishing it from sibling tools like diversity_by_group or diversity_fst.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance for large datasets (use method='allelematrix' + max_markers), but does not compare directly to alternatives or state when not to use this tool. Context implies per-marker focus, but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the algorithm (IBS, UPGMA), output format (Newick), and subsampling behavior. However, it does not disclose potential side effects (file creation) or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two short paragraphs. The first sentence front-loads the core purpose. Every sentence adds value, with no redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and presence of output schema, the description covers output format and handling of large datasets. It lacks explicit prerequisites (e.g., needing a variant set) but is otherwise complete for a file-output tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds algorithmic context (IBS, UPGMA) that helps understand parameter roles, but does not provide new semantic details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool builds a UPGMA dendrogram from IBS distances, a specific verb+resource combination. It distinguishes itself from siblings like diversity_pca and diversity_structure by focusing on tree construction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for phylogenetic analysis but does not explicitly compare to alternative tools or state when to use this over other diversity_* tools. It mentions marker subsampling for large sets, which is indirect guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool lists sequences but does not describe any behavioral traits such as pagination, performance, authentication needs, or whether the output includes both names and IDs. For a simple list operation, this is adequate but not exceptional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary purpose and followed by usage guidance. Every word contributes meaning; no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no enums, output schema present), the description is sufficient. It explains what the tool does and why it's useful. It could be slightly more complete by mentioning the return type, but the context is adequately covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already provides a detailed description for the single required parameter (variant_set_db_id). The description adds no additional meaning beyond what the schema conveys, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('list') and resource ('reference sequences') with context ('available in a variant set'), clearly distinguishing it from sibling tools like count_variants and search_variants which operate on variants, not sequences.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states the tool's purpose: to discover valid 'reference_name' values for region filters on specific tools (count_variants, search_variants, QC & diversity tools). This provides clear context for when to use it, though it does not mention when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the export runs server-side, is streamed to output_path, and can be time-consuming. It also mentions that unavailable formats are reported. However, it lacks details about permissions, data volume limits, overwrite behavior, or error handling. With no annotations, the description bears full burden; it provides useful but incomplete transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two paragraphs: a concise purpose statement followed by a informative block about format and timeout. It avoids fluff and is well-structured, though some repetition with schema descriptions exists (e.g., format default). Overall, it is appropriately sized and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (export, server-side, streaming), the description covers purpose, format variability, timeout, and streaming behavior. An output schema exists, so return values are documented elsewhere. The description is complete enough for a 4-parameter tool with full schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value beyond the schema by explaining format availability (common formats, build-dependent), that the tool reports available formats, and that timeout is in seconds. This provides context not present in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Export a variant set to a file in the given format', specifying the verb (export), resource (variant set), and output (file). Among sibling tools (import, qc, diversity, etc.), export_genotypes is unique in its function, so it is easily distinguished.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for when to use the tool: to export a variant set to a file. It mentions that format availability depends on the Gigwa build and that export can take a while, suggesting raising timeout. However, it does not explicitly state when not to use it or list direct alternatives among siblings, but the guidance is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the alignment process, backend selection (auto/cli/mappy), and marker classification (unique, multi, unmapped). However, it does not mention error handling, performance expectations, or any side effects beyond writing a CSV. Additional details on RAM usage and failure modes would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a concise first sentence summarizing purpose, followed by details on alignment, output, and implementation. It is front-loaded and each part adds value. It could be slightly more concise (e.g., the backend explanation could be shorter), but it remains clear and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, presence of output schema), the description is fairly complete. It covers the main workflow, output format, and integration with another tool. It lacks details on error conditions or input validation, but overall provides sufficient context for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaningful context beyond the schema: it explains that reference_fasta can be a FASTA or .mmi index, backend 'auto' uses CLI if available, and min_mapq thresholds classification. For output_dir, it gives the default path. This adds value for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The tool's purpose is clearly stated: 'Guess genomic positions for DArTseq SNP markers by aligning their tag sequences.' It specifies the action (align/guess), the resource (DArTseq SNP markers), and the output. The description distinguishes from the sibling tool 'import_dartseq' by noting that the result can be passed to it, clarifying the mapping role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool: for mapping DArTseq markers to a reference genome, and that the output can feed into import_dartseq for genome-anchored import. It does not explicitly state when not to use or list alternatives, but the context is clear enough for an agent to decide. More explicit exclusions would improve this dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Though no annotations are provided, the description discloses key behaviors: it can append or clear project data (clear_project_data) and blocking vs non-blocking execution (wait). This covers safety and control aspects well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states core purpose, second elaborates key parameters. No wasted words, effectively front-loaded for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters and presence of output schema (implied), the description covers required parameters, optional ones, and key behaviors (clear, wait). It adequately sets expectations for an import tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so description adds context beyond the schema: explains the role of module/project/run in creating/appending, describes technology as optional free-text, and clarifies clear_project_data and wait. It groups and interprets parameters usefully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it imports a VCF file into Gigwa, specifying the action (import), resource (VCF), and target (Gigwa database/project/run). It distinguishes from sibling import_dartseq by format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Describes parameters like clear_project_data and wait, guiding usage in some scenarios, but does not explicitly state when to use this tool over siblings (e.g., import_dartseq) or exclude alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns IDs, name, and variant/callset counts when available, but lacks details on potential behaviors like network calls or permissions. With no annotations, the description carries full burden but is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action, and no extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an output schema, the description sufficiently explains what the tool returns and how it relates to sibling tools, making it complete for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema coverage, there is no need for parameter explanation. The description adds value by explaining the output context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly specifies that the tool lists every variant set (run) with its exact BrAPI variantSetDbId, and distinguishes itself from list_content by providing IDs directly for use by other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explains that this tool returns IDs needed by other tools and complements list_content, but does not explicitly state when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description effectively discloses that the tool is non-destructive ('does not modify Gigwa'), writes a CSV file, and computes statistics. It does not detail side effects like file overwriting, but the main behavioral traits are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with four sentences, front-loading the core purpose. Every sentence adds value, no redundancy, and the structure is logical.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 params, output schema exists), the description covers the key aspects: purpose, behavior, output file, and performance advice. It lacks detailed CSV format info but the output schema likely covers that, so it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context about the two methods (vcf vs allelematrix) and performance tip, but does not significantly enrich individual parameter meanings beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports markers that would be filtered by MAF/missingness without applying changes, with specific verb and resource. It distinguishes itself from siblings like qc_call_rate by focusing on MAF and missingness filtering, matching the tool name exactly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear when-to-use context: it is a report-only tool for previewing filter outcomes before modification. It also gives guidance for large datasets (pass method='allelematrix' + max_markers). However, it does not explicitly state when not to use it or list alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses server-side computation and no download. Also notes default behavior (total count without filters). Lacks explicit read-only safety statement but adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy. Front-loaded with purpose. Could be slightly shorter but no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists, description doesn't need to cover returns. Input schema well-covered. Lacks performance notes but tool straightforward.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds meaning by explaining filters (genomic region, MAF, max_missing_data) and variant_set_db_id. Also clarifies behavior with no filters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Count variants matching filters' and emphasizes 'computed server-side (nothing is downloaded).' This distinguishes from siblings like search_variants, which likely return actual variant data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Fast way to size a query before pulling data,' providing clear guidance on when to use. Does not mention when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses data processing steps (dropping monomorphic, imputation, scaling), output files, variance explained reporting, and outlier detection. It does not mention any destructive actions, which aligns with a read-only analysis tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise, covering key processing steps and outputs in a few sentences. It front-loads the main purpose. Minor inefficiency: could use bullet points for output files, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 10 parameters and an output schema (though not shown), the description is complete: covers input, processing, output, and optional metadata integration. It addresses outlier detection and scaling, leaving little ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by explaining the purpose of metadata_tsv and group_column for PC plot coloring, and the method parameter for large datasets. It also notes the format of variant_set_db_id, going beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states tool runs PCA on population structure with specific data processing (monomorphic markers dropped, missing mean-imputed, Patterson scaling) and outputs pca_coords.csv. It distinguishes from sibling tools like diversity_by_group and diversity_summary by specifying the PCA scope and details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides clear context for use (population structure PCA) and gives guidance for large sets (method="allelematrix" + max_markers to avoid full VCF export). However, it does not explicitly exclude alternatives or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It states writes variant_search.csv, returns only metadata (id, chrom, pos, ref, alt), no genotypes, and max_variants limit. Could mention overwrite behavior, but overall transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with two sentences and clear bullet points. Front-loaded with purpose. Slightly informal structure but no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters and multiple siblings, the description covers purpose, filter similarities, output format, result cap, and alternative tools. Output schema exists but description adequately explains return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all 9 parameters described in schema). Description adds minimal value by grouping filters as 'Same filters as count_variants', but does not enhance individual parameter meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches variants server-side and writes results to CSV, specifying the filter types and output filename. It distinguishes from siblings by referencing count_variants and QC/diversity tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends using count_variants first to size results, notes max_variants cap, and suggests alternatives for downstream genotype analysis (region/min_maf options on QC/diversity tools).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It describes computations (including rarefied allelic richness for comparability) and output file (diversity_by_group.csv). Lacks details on performance or side effects, but sufficiently transparent for a computation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Concise and well-structured: front-loaded with purpose, then group definition, then specific computed metrics. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, no annotations, but an output schema exists, the description covers main functionality and output. Slightly incomplete about return structure beyond file name, but sufficient for an experienced user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds value by explaining the rarefied metric and group definition methods, going beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it computes per-population diversity metrics (He, Ho, Fis, MAF, % polymorphic, allelic richness) and defines groups similarly to diversity_fst, distinguishing it clearly from siblings like diversity_fst (between-group) and diversity_summary (overall).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explains two ways to define groups (groups_json or metadata_tsv + group_column) and references diversity_fst for consistency. While it doesn't explicitly state when not to use this tool, the context makes its purpose clear relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behavioral traits: pairwise IBS computation, default subsampling, output file generation (duplicate_pairs.csv, duplicate_groups.csv), and the performance-oriented method option. It lacks details on data modification or resource usage, but is otherwise transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with purpose, and each sentence adds value. It efficiently covers purpose, algorithm, defaults, and performance tips without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 6 parameters, 100% schema coverage, and existing output schema, the description is largely complete. It explains output files and method options, though it could mention edge cases like empty results. Still, it is well-rounded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the default max_markers behavior and when to use allelematrix, which goes beyond the individual parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Detect duplicate / clonal accessions via pairwise identity-by-state (IBS)', specifying the verb, resource, and method. It distinguishes from sibling QC tools like qc_call_rate by focusing on duplicate detection, and uses precise language ('core genebank cleaning check').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (for mislabelled duplicates/clones) and provides guidance for large datasets (use method='allelematrix'). However, it does not explicitly mention when not to use it or compare to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool writes a CSV file and flags samples based on a standard deviation threshold. It also notes performance considerations for large datasets, which is a valuable behavioral trait. It does not mention authentication or any destructive actions, but the core behavior is well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: first sentence states the purpose, then provides interpretation of results, and ends with a practical usage tip. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters and an output schema. The description explains the main output, the threshold logic, and gives a performance recommendation. It does not mention that the analysis is cohort-based or requires a variant set ID, but the schema covers the required parameter. Overall, it provides sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the purpose of outlier_sd in context and recommending method='allelematrix' with max_markers for large sets, which goes beyond the schema's simple descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs per-sample observed heterozygosity QC and flags outliers. It distinguishes itself from sibling QC tools by focusing on heterozygosity, and provides interpretation of high and low values, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context by explaining what high and low heterozygosity indicate, helping decide when to use the tool. However, it does not explicitly compare to other QC tools or state when not to use it, though the performance tip for large datasets provides additional guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: reads attributes, writes a specific CSV file (germplasm_metadata.csv), and returns an empty-result note for certain builds (some 2.12). Mentions side effect (file writing) and limitation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. The first sentence is a clear one-liner; the second provides necessary detail on source, output, and edge case. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has few parameters and an output schema (context indicates output schema exists), the description covers the core functionality, side effect (writing CSV), downstream usage, and a known limitation. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (both parameters described). Description adds value by explaining output_dir default and variant_set_db_id format (MODULE§project§run), and that output is one row per accession.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches per-individual metadata (germplasm attributes) from Gigwa and writes a CSV. It distinguishes from siblings like import_metadata (which imports) and diversity tools (which consume the output).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context that metadata must be imported first via import_metadata or BrAPI, and that the CSV output feeds into grouping tools (diversity_fst/diversity_by_group). Does not explicitly state when not to use, but covers key usage conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It transparently describes outputs (two CSV files), return summary contents, and the effect of the method parameter (full export vs. estimation). It does not mention authorization or side effects beyond file writing, but given the absence of annotations, this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph but well-structured: purpose first, then outputs, then parameter clarifications. It is efficient with no wasted words, though slightly dense. Could benefit from bullet points for readability, but still concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters with full schema coverage and an output schema, the description covers all necessary aspects: tool purpose, output files, parameter explanations, and usage advice. It is complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema: it explains the source of variant_set_db_id (from list_content), clarifies the allelematrix method for large sets, specifies the 1-based coordinate system for region, and describes the max_markers cap. This helps the agent select appropriate parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs per-sample and per-marker call rate QC, including flagging samples/markers below thresholds, writing specific output files, and returning a summary. It distinguishes itself from sibling QC tools like qc_duplicate_accessions or qc_maf_filter by its specific focus on call rate/missingness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use alternative methods (e.g., 'For large production sets pass method="allelematrix"') and explains the region parameter restriction. However, it does not explicitly state when not to use this tool compared to other QC tools, nor does it list prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it asks Gigwa to cancel the process, returns whether the request was accepted, and recommends confirmation via polling. This adequately covers the tool's actions and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the main purpose, then providing necessary details about usage and follow-up. Every sentence is informative and concise with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single parameter, no annotations, and an output schema (implied), the description sufficiently covers the return value and next steps. It is complete for a straightforward cancellation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage with a description of the progress_token format. The description adds crucial context about the token's origin (from imports with wait=False) and its purpose, enriching the parameter's meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Abort a running import (or other long process)' with the specific input 'progress token'. It distinguishes from sibling tools like import_dartseq, import_vcf, and get_import_progress by focusing on cancellation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (to cancel a running import) and how to obtain the progress token (by setting wait=False on import calls). It also advises polling get_import_progress to confirm cancellation, providing clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description fully carries burden. It discloses read-only nature ('never modifies Gigwa'), output file creation, bounded sampling, constant cost, and detection algorithms (HWE test, thresholds). Highly transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly long but well-structured: first sentence defines purpose, then parameter details, then algorithm and output. Some redundancy (e.g., mentions 'writes CSV' twice) but front-loaded and logically organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (7 parameters, no required), high schema coverage, and presence of output schema, the description is extremely complete. It covers algorithm, output format, detection criteria, and parameter effects, enabling confident agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds meaning beyond schema by explaining parameter roles (e.g., variant_set_db_id selects specific run), threshold semantics, and output file name. It enriches understanding of how each parameter affects behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Scan a Gigwa instance for databases imported with genotype-encoding artifacts' and distinguishes from sibling tools like import_vcf or diversity tools by focusing on import quality detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear when-to-use: 'With no variant_set_db_id this audits every run; pass one to audit a single variant set.' It also explains the sampling strategy and cost, implying safe for production, but lacks explicit when-not-to-use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It details credential handling (environment variables, never passed through chat), anonymous access, and the connection verification with rollback on failure. This is excellent transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections and front-loaded purpose. It is slightly verbose but every sentence adds value. Minor improvements could trim redundancies, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (credential security, environment variables, connection verification), the description covers all behavioral aspects. With no annotations and no output schema needing explanation, it is fully complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 100% schema coverage, the description adds significant value: explains that bare host:port assumes https, elaborates on profile vs anonymous vs default credentials, and clarifies credential precedence. The schema alone would not convey these safety semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it switches the active Gigwa server at runtime without restart. The verb 'switch' combined with 'active Gigwa server' precisely defines the action and resource. It differentiates itself from sibling tools which are about data import, query, or analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear guidance on when to use (to connect to a server) and how credentials are resolved. It implies not to use when you want to remain on the current server, but lacks explicit 'when not to use' statements. Still highly informative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses generation of auth token and reporting of URL/version. Honest about 'best-effort' version. No annotations, but description sufficiently covers behavioral traits for a connectivity check.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise, informative sentences with no waste. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Fully explains behavior, side effects, and usage context. Complete for a simple connectivity tool with output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so baseline is 4. Description adds value by explaining output (URL, version) beyond empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Check connectivity' and resource 'configured Gigwa server'. Distinguishes from siblings like import or diversity tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this first to confirm the connection works before importing data', providing clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the conversion process (2-row genotype calling), the effect of reference_fasta (genome-anchored vs Unmapped), and the behavior of positions_csv. It also describes async behavior with wait=False. However, it does not mention error handling, idempotency, or performance, which are minor gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence followed by paragraphs each addressing a specific aspect (conversion, file inputs, anchoring, parameters). It uses efficient language without redundancy, and every sentence contributes necessary information. Though long, it is appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 13 parameters and 3 required, the description covers all major behavioral aspects: input requirements, conversion details, anchoring options, and key flags. The presence of an output schema (not shown) likely covers return values, so description completeness is high. It addresses the main use cases and parameter interactions, leaving few gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing baseline of 3. The description adds substantial value beyond schema: explains the requirement of at least one of snp_xlsx/silico_xlsx, the implications of combining both, the details of .mmi vs FASTA for reference_fasta, and the mutual exclusivity with positions_csv. This addition elevates the score to 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: importing DArTseq data from xlsx reports into Gigwa. It specifies the verb ('Import'), resource ('DArTseq data from xlsx report(s)'), and destination ('Gigwa'). It distinguishes from siblings by explaining the specific file types and conversion process, and notes the unusual case of combining SNP and Silico-DArT in one run, implying separate tools for other formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool: at least one xlsx file must be provided, and warns against combining SNP and Silico in the same run unless intentional. It explains the alternative of using reference_fasta vs positions_csv and their mutual exclusivity. Also details the effects of clear_project_data, skip_monomorphic, and wait, giving clear context for decision-making.
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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