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

find_tool

Read-onlyIdempotent

Search available MCP tools by keyword or category before calling them. Returns matching tool names, descriptions, and optionally their inputSchemas. Call this when you are unsure which tool to use or want to explore the catalogue. Categories: data, encoding, text, llm, qa, rag, dev, security, web.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesKeyword(s) to search in tool name and description (e.g. "cors", "token", "vector", "json")
categoryNoOptional: filter by category — data | encoding | text | llm | qa | rag | dev | security | web
max_resultsNoMaximum tools to return (default 10, max 50). Results are ranked by IDF-weighted relevance, so common words like "test" do not inflate the list.
with_schemaNoSet true to include inputSchema in results (default: false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
toolNo
countNo
queryNo
scoreNo
toolsNo
categoryNo
truncatedNo
total_matchesNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds behavioral context such as ranking by IDF-weighted relevance and that common words do not inflate results. It also notes the optional inclusion of inputSchemas in results, which is a useful behavioral detail beyond the 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?

The description is three sentences, each earning its place: the first states the core action and timing, the second describes the return content, and the third provides the category list. There is no redundancy or filler.

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

Completeness5/5

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

Given the presence of an output schema, full parameter documentation, and annotations, the description supplies the missing contextual information: when to invoke it and how results are ranked. It covers the tool's role in the larger catalogue and does so without needing to explain return structures.

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

Parameters3/5

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

Schema description coverage is 100%, so each parameter (query, category, max_results, with_schema) is already well-documented in the input schema. The description adds no additional parameter-specific meaning beyond what the schema provides. It does list the allowed categories, but this is also present in the schema description for the category parameter.

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 'Search' with a clear resource ('available MCP tools') and includes the scope ('by keyword or category before calling them'). It also distinguishes itself from siblings by emphasizing it is a catalogue exploration tool, not a functional tool. The return value (tool names, descriptions, optionally schemas) is stated directly.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool: 'Call this when you are unsure which tool to use or want to explore the catalogue.' This provides clear context for use. It does not explicitly mention when not to use it or list alternative tools, but given the meta-purpose, this is sufficient.

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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