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IA-QA — 130+ QA & Dev Tools for AI Agents

fetch_jira_issue

Read-only

Fetch a complete Jira issue: summary, description converted to Markdown, status, assignee, priority, labels, custom fields, and optionally comments and attachment metadata. BYOK — credentials transit in-memory only, never stored on ia-qa.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoSpecific Jira field names to return. Omit for all standard fields.
issue_keyYesJira issue key, e.g. "PROJ-123"
jira_emailYesAtlassian account email
jira_tokenYesAtlassian API token (from id.atlassian.com > Security > API tokens)
jira_base_urlYesAtlassian base URL, e.g. "https://mycompany.atlassian.net"
include_commentsNoInclude issue comments, up to 20 (default: true)
include_attachmentsNoInclude attachment metadata list (default: false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
urlNo
typeNo
labelsNo
statusNo
summaryNo
assigneeNo
priorityNo
reporterNo
descriptionNo

TDQS

A4.2/5.0
Behavior5/5

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

The description adds meaningful context beyond annotations: description is converted to Markdown, comments/attachments are optional, and BYOK credentials transit in-memory only and are never stored on ia-qa.com. This is consistent with readOnlyHint=true and non-destructive behavior, and it provides important security/formatting details not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose, followed by a concise security note. Every clause earns its place with no fluff or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, key output transformations, optional inclusions, and credential handling. With an output schema present, it need not describe return structure. It is adequately complete for a 7-parameter tool with strong annotations, though it could briefly mention prerequisites like network access or Jira permissions.

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

Parameters3/5

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

Schema coverage is 100% and all parameters have descriptions, so the baseline is 3. The description adds context about output contents (Markdown conversion, custom fields, optional comments/attachments) but does not materially extend parameter semantics beyond what the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Fetch' and clearly identifies the resource as 'a complete Jira issue' with an explicit enumeration of returned contents (summary, description converted to Markdown, status, assignee, priority, labels, custom fields, optional comments/attachments). This distinguishes it from sibling tools like search_jira_issues and post_jira_comment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving full Jira issue details and optional comments/attachment metadata, but it does not explicitly state when to prefer this over search_jira_issues or whether to use it before post_jira_comment. There is clear context but no exclusions or alternative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

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