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

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 value by specifying the statistical metrics returned (mean, median, std deviation, winner, improvement percentage), which goes beyond the structured annotations. No contradictions.

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

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

The description is a single, well-structured sentence that efficiently covers purpose, input, and output without fluff. It is front-loaded with the primary action 'Generate an A/B test report'.

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

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

Given the presence of an output schema and simple input schema, the description adequately covers what the tool does. It could mention edge cases like equal scores or unequal array lengths, but for a straightforward statistical tool, it is sufficiently complete.

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

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

Schema coverage is 100%, but the description adds semantic meaning by calling the variants 'prompts or model configurations', which is not explicit in the schema's generic 'variant configuration'. It also clarifies that 'scores' are arrays for statistical comparison, aiding understanding.

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

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

Description clearly states the tool's function: 'Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison' with specific outputs. This distinguishes it from sibling tools like compare_models or compare_responses, which likely have different comparison methods.

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

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

It clearly implies when to use: when you have two sets of scores to compare statistically. It mentions 'two prompts or model configurations' and 'arrays of scores', which is sufficient context. It doesn't explicitly name alternatives, but the usage context is clear.

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

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses significant behavioral traits: it explicitly says 'It does NOT do data-flow analysis', cannot follow values across functions/files, and that empty results are not safety verdicts. It also notes the response lists what it did not analyse, which is rich, actionable transparency.

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

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

The description is appropriately sized and front-loaded; it starts with 'Pattern-based diff linter', then lists the targeted patterns, cites line-level findings, and clarifies limitations and advisory nature. While dense, every sentence is informative and the structure is logical. It loses one point for being slightly long relative to the simplest possible phrasing.

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

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

Despite an existing output schema, the description covers all essential information: what it does, which patterns it flags, how results are presented, what it does not do, and its advisory role. This is complete for an agent to select and invoke the tool correctly without relying on the output schema.

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

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

Schema description coverage is 100%, so the schema already documents the meaning of context, version1, and version2. The description adds only the general diff-linting context, which does not materially enhance parameter understanding 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.

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

The description defines it as a 'Pattern-based diff linter' that 'flags a fixed set of risky shapes in changed code', then enumerates specific patterns like SQL/shell interpolation, eval/new Function, and empty catch blocks. This is a specific verb+resource (lints diffs for known risky patterns) and clearly distinguishes it from siblings such as diff_text or analyze_responses.

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

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

It states the tool is 'Advisory triage — use a static analyser for a real security gate', providing an explicit when-not-to-use and an alternative. It also clarifies that it does not do data-flow analysis and that an empty result is not a safety verdict, guiding agents on appropriate reliance.

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

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds valuable behavioral traits beyond this: 'Deterministic, no LLM, no key — gate-able in CI.' It also explains the algorithmic behavior (consensus, pairwise cosine, ROUGE-L composite, closest/outlier). No contradiction with annotations.

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

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

Five sentences, front-loaded with the core purpose. Each sentence earns its place: no-reference behavior, reference behavior, determinism/no-key/CI guarantee, input requirement, and sibling tool alternative. No redundancy or fluff.

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

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

For a semantically complex tool, the description covers use cases, input constraints, behavioral guarantees, and alternatives. An output schema exists, so return-value details are already structured. The description is fully complete without being verbose.

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

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

The input schema covers 100% of parameters and already describes the reference behavior ('If set, each output is also ranked by closeness to it...'). The description reinforces output semantics ('same task', '2+', 'reference') but adds no significant syntax or format details beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description begins with a specific verb and resource: 'Semantically analyze N already-produced model outputs for the SAME task.' It clearly distinguishes this tool from the sibling compare_responses by explicitly recommending that alternative for a different use case. The scope and function are unambiguous.

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

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

Provides explicit usage context: 'the MCP counterpart to the LLM Sandbox' and 'You bring the outputs (2+)'. It also names an alternative with a precise exclusion: 'For a 2-way head-to-head with structural JSON diff use compare_responses instead.' This fully guides when to use the tool versus alternatives.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds that the output is UTF-8 text, clarifying that this is not binary decoding, and lists common input sources. It does not discuss error behavior on invalid Base64, but the annotation coverage lowers the burden.

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

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

Two sentences: the first states the core action, the second gives practical use cases. No redundant wording or filler.

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

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

For a simple single-parameter utility with robust annotations and an output schema, the description covers purpose, usage context, and output format sufficiently. No additional details are necessary.

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

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

Schema coverage is 100% with the parameter described as 'Base64 string to decode'. The description reinforces this but does not add new semantic details beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description opens with 'Decode a Base64 string back to UTF-8 text', clearly stating the verb, resource, and output format. The use cases ('API responses, JWT payload claims, config file values, or attachment data') add specificity and distinguish it from sibling decode tools like decode_jwt, which targets JWT specifically.

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

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

The phrase 'Use for inspecting...' gives explicit context and typical scenarios. However, no alternatives or when-not-to-use conditions are mentioned; it doesn't explicitly compare to decode_jwt or base64_encode, so it stops short of full exclusion guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is well covered. The description adds the input encoding detail (UTF-8) and typical use cases, but does not disclose additional behavioral traits such as exact output format or handling of edge cases. This is adequate but not rich.

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

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

Two concise sentences: the first states the action, the second provides usage rationale. No fluff or repetition of schema/annotations.

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

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

For a single-parameter encoding utility with solid annotations and an output schema, the description fully covers purpose and when to use it. No further explanation is necessary.

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

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

Schema description coverage is 100% ('Text to encode'). The description adds the meaningful detail that the input must be a UTF-8 string, which clarifies behavior for non-ASCII text beyond the schema's vague 'Text to encode.'

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

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

The description clearly states the verb ('Encode') and the specific resource ('a UTF-8 string to Base64'). It is immediately distinguishable from sibling tools like base64_decode and url_encode.

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

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

The description explicitly says 'Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data URIs.' This provides clear context, though it does not mention alternatives or when not to use the tool.

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

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint. The description adds valuable context beyond annotations: it runs entirely locally and requires no API key, plus details the return structure. This complements annotations without contradicting them.

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

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

Two sentences, front-loaded with purpose, followed by outputs and a key execution detail. No filler—every sentence adds value.

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

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

With a single well-documented parameter, output schema present, and annotations covering safety, the description fully addresses the tool's complexity. It explains inputs, outputs, and execution context, making it complete.

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

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

Schema covers the single parameter 'responses' with descriptions. The description adds meaning by requiring responses to come from the same prompt template with demographic variants, clarifying the expected input beyond the schema's generic 'variant responses'.

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

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

The description clearly states the tool's function: analyzing LLM responses with demographic variants for bias. It specifies the input type (same prompt template with different variants), the output (bias score, sentiment, similarity, verdict), and differentiates from generic siblings like analyze_responses or compare_responses.

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

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

It provides clear context for when to use (responses from same prompt template with demographic variants) and what it returns. However, it does not explicitly mention alternatives or when not to use, which prevents a 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that scores are normalized and it handles one or more documents, but does not detail other behavioral traits like tokenization or comparability. No contradiction with annotations.

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

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

Two sentences with the main action front-loaded. The second sentence provides useful context about the algorithm's industry usage, though it could be trimmed slightly. Overall very concise with no wasted words.

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

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

With an output schema present and annotations covering read-only/idempotent safety, the description adequately covers the tool's purpose and return behavior. It lacks explicit alternative guidance but is sufficiently complete for a computational scoring tool.

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

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

Schema description coverage is 100%, providing descriptions for all parameters (query, documents, top_k, k1, b). The description adds no additional parameter-specific meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states 'Compute BM25 relevance score between a query and one or more documents', identifying the specific verb, resource, and unique algorithm. This distinguishes it from siblings like rag_relevance_rank or similarity_score by naming BM25 explicitly.

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

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

It provides clear context by stating BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, OpenSearch, and Weaviate hybrid search, implying when to use it. However, it does not explicitly name alternatives or exclusions such as 'for semantic similarity use embedding_similarity instead'.

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

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

Annotations already establish read-only/idempotent/non-destructive safety, so the bar is lower. The description adds valuable behavioral context by enumerating internal steps (token budgeting, citation numbering, system instruction injection, source attribution), which helps predict output structure. It doesn't disclose edge-case behaviors (e.g., truncation strategy) but exceeds baseline.

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

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

Two sentences, front-loaded with the core action. Every phrase adds information—no filler or redundancy. Appropriate length for the tool's complexity.

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

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

The description plus rich schema and output schema provide enough for selection and basic invocation. It covers the main functional areas and hints at all parameter roles. It omits details like truncation strategy and citation ordering, but these are secondary given the strong schema context.

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

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

Schema covers 100% of parameters, so baseline is 3. The description adds meaning by connecting parameters to their effects: max_context_tokens↔token budgeting, cite_sources↔citation numbering, system_instruction↔system instruction injection, chunks.source↔source attribution. This contextualizes the schema without duplicating it.

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

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

The description opens with 'Assemble a complete RAG prompt', combining a specific verb with resource and data source. The additional capabilities (token budgeting, citations, system instruction) distinguish it from sibling tools like few_shot_formatter or system_prompt_builder.

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

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

States it operates on 'retrieved context chunks', making the input precondition clear. However, it doesn't explicitly name alternatives or state when not to use it, though the name and RAG specificity imply these boundaries.

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

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

Annotations already declare readOnlyHint true, idempotentHint true, and destructiveHint false, covering safety and side effects. The description adds no additional behavioral context (e.g., language limitations, score ranges, or empty-input handling), but this is a simple read-only operation. It adds no contradictions.

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

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

The description is two sentences, front-loaded with the action and metrics, and the second sentence adds a practical use case. No wasted words or redundant information.

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

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

For a simple one-parameter read-only tool with an output schema and full annotations, the description is complete. It specifies what it does, names the metrics, and gives a usage context; return values are covered by the output schema.

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

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

Schema description coverage is 100% for the single 'input' parameter, which is described as 'Text to analyze for readability'. The tool description does not add extra parameter semantics beyond restating the purpose, so it meets the baseline without enhancements.

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

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

The description clearly states a specific action ('Calculate readability scores') and enumerates the exact metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, Automated Readability Index). This distinguishes it from sibling text-analysis tools like text_stats or count_tokens.

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

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

The description provides a clear use case ('Useful for evaluating LLM output quality'), indicating when to use it. It does not explicitly exclude alternatives, but no competing readability tool exists among siblings, so the context is sufficient.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context by specifying all supported conversions, which directly informs the agent of expected transformations. No contradictions exist.

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

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

Two concise sentences with the primary action front-loaded. Every word adds value; the list of conventions and use-case phrase are efficient and clear. No redundancy or filler.

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

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

This is a simple two-parameter, deterministic conversion tool. The schema fully documents parameters, annotations cover safety, and the description lists all supported cases. With an output schema present, no further return-format details are needed.

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

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

Schema description coverage is 100% with both 'input' and 'to' documented, including examples. The description restates the list of target cases already present in the schema's 'to' description, adding minimal new semantic detail beyond what the schema provides.

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

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

The description opens with 'Convert a string between naming conventions,' using a specific verb and resource while enumerating all targeted cases (camelCase, PascalCase, snake_case, etc.). This clearly differentiates it from sibling conversion tools like base64_decode or number_base_convert, which handle other formats.

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

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

The phrase 'Essential for code generation and refactoring' provides a clear use case, indicating when to apply the tool. It does not explicitly name alternative tools but the strong context and enumerating of naming conventions make the usage obvious. Lacks an explicit when-not-to-use statement.

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

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds behavioral output context by stating it returns compliance for AA/AAA normal and large text, which is useful beyond the annotations. It does not mention edge cases like non-hex input, but this is a low-risk calculation tool.

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

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

Two sentences, front-loaded with the action, no fluff. Every word adds value, and it clearly states inputs and outputs.

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

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

For a simple calculation tool with complete schema, annotations, and an output schema, the description covers purpose and return semantics. No additional context is necessary; it is fully adequate for agent use.

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

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

Schema description coverage is 100% with both params (foreground/background) documented as hex strings. The description only says 'two colors', adding little beyond the schema. Baseline 3 applies since schema fully covers parameters.

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

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

The description uses a specific verb 'Calculate' and names the resource 'WCAG 2.1 contrast ratio between two colors', clearly distinguishing it from sibling tools like color_convert. It also states the return value (ratio and compliance), fully clarifying intent.

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

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

The description implies when to use the tool (for checking WCAG accessibility contrast) and context of compliance levels. It does not explicitly exclude alternatives or name siblings, but the context is clear enough for an agent to select it appropriately.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read operation. The description adds accepted input formats but does not disclose additional behavioral traits (e.g., output behavior, error handling). No contradiction.

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

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

Three concise sentences with no filler: the first states purpose, the second gives usage context, and the third specifies accepted formats. Every sentence earns its place.

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

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

For a simple one-parameter converter with a present output schema and thorough annotations, the description adequately covers purpose, usage, and input formats. No critical gaps are apparent.

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

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

The single parameter 'input' is fully described in the schema with concrete examples (100% coverage). The description adds a compact pattern summary, but this overlaps with the schema's examples, so the added value is marginal.

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

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

The description starts with a specific action 'Convert a color between HEX, RGB, and HSL formats', naming both the resource (color) and the exact formats. This clearly differentiates it from sibling tools like case_convert or base64_encode.

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

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

Explicitly states when to use the tool (translating design tokens, verifying accessibility, normalizing user input) but does not mention when not to use or mention alternatives such as check_contrast_ratio, which is a related sibling.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds behavioral context beyond annotations: it enforces a 2-5 model range (not in schema) and indicates the tool returns a recommendation based on use case. It also lists the comparison dimensions, which clarifies expected behavior.

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

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

The description is a single, front-loaded sentence that efficiently conveys purpose, scope, and output. Every clause adds value, with no redundant information or filler.

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

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

The description is complete for a read-only comparison tool with an output schema. It states the input range and criteria, the output type (comparison table with recommendation), and the use-case parameter. Since annotations cover safety and an output schema exists, no further details are necessary.

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

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

Schema description coverage is 100%, so both 'models' and 'use_case' are already explained. The description adds value by specifying the 2-5 count constraint on 'models' and clarifying that 'use_case' optimizes the recommendation, which goes beyond the schema's enum descriptions.

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

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

The description clearly states the tool's function: 'Compare 2-5 AI models side by side' with specific attributes (context window, pricing, multimodal, reasoning, provider). It uses a specific verb and resource, and it distinguishes itself from siblings like 'compare_responses' (which likely compares outputs) and 'model_info' (single model info).

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

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

The description implies when to use the tool ('based on your use case') and lists the comparison criteria, providing clear context. However, it does not explicitly mention alternatives or exclusion conditions, such as when to use 'compare_responses' instead. No explicit 'when not to use' guidance is given.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: it lists exactly what metrics are returned, explains that a `reference` causes scoring against ground truth, and that providing `model`+`api_key` triggers an LLM judge call. It also notes 'No re-execution' and the schema notes the API key is 'never stored,' which bolsters transparency.

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

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

The description is compact—five sentences that front-load the core purpose, then layer conditional behavior and end with a clear constraint. Every sentence earns its place; no filler or redundancy.

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

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

Given an output schema, rich annotations, and 100% schema parameter coverage, the description is complete enough. It covers the two major optional modes (reference-based scoring and LLM judging), the deterministic metrics, and the no-re-execution constraint, leaving no significant gaps for a tool of this complexity.

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

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

Schema coverage is 100% with good per-parameter descriptions, yielding a baseline of 3. The description adds semantic value by explaining how parameters interact: `reference` makes each output scored against it, `model`+`api_key` enables the judge, and the metric list (token cosine, ROUGE-L, Jaccard, JSON diff) clarifies what the tool computes from `response_a` and `response_b`.

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

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

The description opens with a specific verb and resource: 'Compare two ALREADY-PRODUCED outputs ... side by side.' It clearly differentiates from siblings by emphasizing no re-execution and listing concrete deterministic metrics plus an optional LLM judge verdict.

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

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

The description clearly states when to use the tool: when outputs already exist and you want a side-by-side comparison. It gives conditional guidance for `reference` and `model`+`api_key`, and explicitly says 'No re-execution — you bring the outputs,' which implies it is not for generating outputs. It does not name alternatives explicitly, so it's strong context without formal exclusions.

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

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

Goes beyond the annotations by revealing the algorithmic limitation (surface-level word matching) with a concrete example. It also adds traits such as 'fast' and 'deterministic', which are not present in the annotations but are important behavioral context. No contradictions with annotations.

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

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

The description is extremely concise, using only two sentences to convey purpose, method, limitations, and alternatives. Every sentence adds value, and the text is well-structured with a clear separation of strengths, limitations, and use case.

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

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

The tool is simple, has full parameter documentation, and an output schema. The description covers use cases, limitations, and alternatives, making it complete for an agent to decide when to use it. There are no obvious gaps given the available structured information.

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

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

The schema already provides 100% coverage for both parameters, so the baseline is 3. The description adds slight context by explaining that check_facts deals with 'fact drift (number comparison)', but this is already implied by the schema description. No additional parameter-level detail is provided in the description.

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

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

The description clearly states the tool's purpose: compare multiple LLM responses to the same prompt and detect inconsistencies via Jaccard similarity and fact drift. It names specific methods and distinguishes from sibling tools like run_semantic_tests by explicitly referencing embedding mode for semantic consistency.

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

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

Provides clear when-to-use context: 'Essential for determinism testing' and highlights the fast/deterministic/no-API-key benefits. It explicitly states when not to rely on it (semantic equivalence) and directs to run_semantic_tests as an alternative for true semantic consistency.

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

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

Annotations already declare read-only and idempotent behavior, so the bar is lower. The description adds valuable context: the operation is an 'estimate' (implying approximation) and it 'warns about truncation risk'. This goes beyond the schema and annotations without contradicting them.

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

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

The description is two sentences, front-loaded with the most important action, and contains no filler or redundant repetition of annotations. Every word contributes to understanding the tool's purpose.

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

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

Given the tool's moderate complexity, full schema coverage, and presence of an output schema, the description sufficiently covers inputs, purpose, and key behavior. It does not need to describe return values because the output schema handles that, and it adequately sets expectations about estimation and warnings.

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

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

Schema description coverage is 100%, so the baseline is 3. The description repeats the message structure already present in the schema and does not add new parameter details. It provides no additional syntax or format guidance beyond what the schema already documents.

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

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

The description clearly states the tool's function with specific verbs ('estimate', 'check', 'warns') and names its resource (message array and target model's context window). It distinguishes itself from siblings like count_tokens by adding the context-window fit check and truncation warning.

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

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

The description implicitly communicates when to use the tool (when you need to estimate token usage and verify context fit), but it does not explicitly name alternatives or exclusions. Given the clear context stated, this meets the 'clear context, no exclusions' level.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable behavioral context by specifying the input format (messages array) and the exact analysis dimensions. It does not contradict annotations and provides extra detail about what the tool evaluates.

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

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

The description is concise, with three sentences each serving a distinct purpose: stating the core action, specifying input format, and giving usage context. It is front-loaded with the primary verb and resource, and every sentence earns its place.

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

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

The tool has a single well-documented parameter, a full schema, clear annotations, and an output schema. The description covers purpose, input format, and use case, which is sufficient for an agent to select and invoke it correctly. Minor gaps like message ordering constraints are not mentioned, but the tool's simplicity makes these non-critical.

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

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

The schema has 100% coverage for the single 'messages' parameter, which is already described as 'Conversation messages in order'. The description repeats the array format '[{role, content}]' but adds no additional semantic info beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose with a specific verb ('Analyze') and resource ('multi-turn conversation'), and lists concrete analysis dimensions (context retention, topic drift, instruction following, repetition). It also mentions the input format and the primary use case (chatbot QA), which distinguishes it from sibling tools like analyze_responses or consistency_check.

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

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

The description says 'Essential for chatbot QA', providing a clear context for when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of full differentiation but still gives strong usage context.

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

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

Annotations already declare readOnlyHint true and destructiveHint false, and the description adds significant behavioral detail: it fetches the URL, inspects all Set-Cookie headers, flags insecure patterns, and returns per-cookie grades plus an overall score. It does not contradict annotations and explains the non-destructive yet network-active nature.

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

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

The description is two sentences, densely packed with specific security checks and output details. It is front-loaded with the purpose and every clause adds value, though it is slightly longer than the minimal necessary. No wasted words.

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

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

Given the tool complexity and existence of an output schema, the description thoroughly covers what it does, what it examines, what it flags, and what it returns. It is complete for an agent to invoke successfully and understand the result layout, even without reading the output schema.

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

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

The input schema already documents the single 'url' parameter with full coverage (100%), including an example. The description reinforces that any URL is accepted but adds no new semantic details beyond the schema, so the baseline score of 3 applies.

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

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

The description clearly states the tool audits cookie security attributes by fetching a URL and inspecting Set-Cookie headers. It lists specific attributes checked and flags found, and the focus on cookies distinguishes it from sibling tools like security_headers_check and web_security_audit.

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

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

The description provides clear context for when to use the tool (auditing cookie security on any URL) but does not explicitly name alternatives or exclusions. The context is unambiguous enough for an agent to select it for cookie-specific checks, but adding a note about when to prefer it over web_security_audit would make it stronger.

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

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

The description adds meaningful behavioral context beyond the annotations: it states the tool simulates a browser, returns the main response status, all Access-Control-* headers, the tested origin, and the preflight OPTIONS response. This informs the agent about the tool's output and behavior, complementing the readOnlyHint and destructiveHint annotations.

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

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

The description is two sentences, front-loaded with the primary purpose, then lists return values and usage guidance. Every sentence adds value with no redundancy or fluff.

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

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

With an output schema present and full schema coverage, the description covers purpose, behavior, and typical usage. It's complete for a simple diagnostic tool, though it could optionally mention error handling or network failure behavior for full completeness.

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

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

The input schema already provides descriptions for all three parameters (url, method, origin), with 100% coverage. The description doesn't add any additional parameter-specific details, so it meets the baseline but doesn't exceed it.

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

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

The description clearly states the tool's function: checking CORS configuration like a browser would. It mentions the resource (URL) and specific action, but doesn't explicitly distinguish itself from the sibling 'cors_test' tool, only from security auditing in general.

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

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

The description gives a clear usage context ('Use this for direct CORS debugging') and hints at an exclusion ('not just security auditing'). However, it does not name specific alternative tools, so it lacks explicit when-not-to-use instructions relative to siblings.

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

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

Even though annotations declare readOnlyHint=true and destructiveHint=false, the description goes beyond by explicitly disclosing it 'Sends preflight (OPTIONS) and cross-origin requests with various Origin headers' - acknowledging that this tool makes network calls. It also details the detection logic (wildcard origins, origin reflection, null origin, subdomain wildcard, missing Vary headers), providing meaningful behavioral insight 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.

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

The description is four sentences long, opens with a clear front-loaded statement of purpose, and efficiently lists the key detection checks and return values. The enumeration of five specific misconfiguration types is somewhat dense but earns its place as it specifies the tool's coverage. It could be slightly more concise by trimming the 'Essential for API security audits' filler, but it remains well-structured.

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

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

The description fully covers the tool's scope and outputs: it lists the tested misconfigurations, states the returned risk levels (safe/low/medium/high/critical), per-test results, and fix recommendations. An output schema exists to capture structure, so the description doesn't need to repeat fields. Given the tool's complexity, this is a complete and self-sufficient description.

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

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

Schema coverage is 100%, with both 'url' and 'origin' parameters already documented in the schema. The description does not add new parameter-level details beyond what the schema states, though it does mention 'with various Origin headers' which hints at the auto-testing behavior when 'origin' is omitted. This meets the baseline but doesn't exceed it.

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

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

The description opens with 'Test a URL for CORS misconfigurations', a specific verb+resource statement that clearly distinguishes this tool from siblings like cors_checker or web_security_audit. It further enumerates concrete detection capabilities (wildcard origins, origin reflection, etc.) and states it returns risk level, per-test results, and fix recommendations, making its purpose unmistakable.

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

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

The description provides a clear when-to-use signal by stating 'Essential for API security audits', and it enumerates the exact types of CORS misconfigurations it detects, helping an agent choose it for security scanning tasks. However, it does not explicitly mention when to prefer an alternative such as cors_checker or what exclusions apply, so it falls short of a 5.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds valuable behavioral insight by listing what the analysis detects (step count, backtracking, reasoning depth, etc.), which goes beyond the structured fields and helps the agent anticipate the tool's output dimensions.

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

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

Two tightly written sentences with no filler. The first sentence states the action and key capabilities; the second gives the evaluation context. Every clause adds value.

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

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

The description is complete for a tool with rich annotations and an output schema. It explains what the tool does, what it detects, and when it is useful. There is no missing context about safety or return values because annotations and output schema cover those aspects.

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

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

Schema description coverage is 100%, so the baseline is 3; the description does not need to repeat parameter details. It adds implicit alignment by mentioning 'conclusion presence' which relates to the optional expected_conclusion parameter, but does not explicitly explain parameter semantics beyond the schema.

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

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

The description uses a specific verb 'Analyze' with a precise resource ('Chain-of-Thought (CoT) or reasoning trace') and enumerates concrete detection outputs (step count, logical flow, conclusion presence, backtracking, reasoning depth). It also distinguishes this tool from siblings by explicitly targeting reasoning traces and naming model families (o1/o3/DeepSeek-R1) for evaluation.

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

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

The description provides clear context by stating the tool is 'Useful for o1/o3/DeepSeek-R1 evaluation,' which signals when to use it. It does not explicitly mention when not to use it or suggest alternatives, but the use case is specific enough to guide selection.

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

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

Annotations already declare this as a safe, read-only, idempotent operation, so the description need not restate those traits. It adds value by specifying the exact outputs (comments, blank lines, density) and supported language families, which informs the agent about the tool's capabilities beyond the schema.

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

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

The description is a compact two-sentence summary: the first sentence states the core function and output metrics, the second lists supported languages. There is no fluff or redundant information; every sentence contributes to understanding the tool.

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

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

With a read-only annotation, a well-defined schema, and an output schema present, the description covers the essential context: what the tool counts, which languages it supports, and the output metrics. For a simple analysis tool of this complexity, no additional behavioral details are necessary.

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

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

Both parameters are fully described in the input schema with clear descriptions (source code string and optional language hint), providing 100% schema coverage. The tool description does not add additional parameter semantics, so the baseline score of 3 is appropriate.

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

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

The description opens with 'Count lines of code' and enumerates the specific metrics (total, code, comment, blank, density), making the tool's function unambiguous. It also lists supported languages, which distinguishes it from generic text analysis tools like text_stats or count_tokens.

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

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

The description implies usage for code analysis but does not explicitly state when to use it over alternatives or when not to use it. There is no mention of sibling tools such as count_tokens or calculate_readability, leaving the choice to the agent's inference.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds the approximation method (cl100k_base, ~4 chars/token), which sets expectations about accuracy, and discloses the return fields. This adds meaningful behavioral context beyond the annotations without contradicting them.

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

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

The description is two sentences with no filler. The first sentence states the action and method; the second provides usage timing and output summary. Every word earns its place.

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

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

The tool is simple with one parameter and an output schema. The description covers purpose, usage timing, approximation details, and return fields, making it complete for an agent to select and invoke correctly. The existing output schema handles return value schemas, so no additional return documentation is needed.

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

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

The schema covers 100% of the parameter with a basic description. The tool description adds no additional detail about the input parameter (e.g., length limits or encoding), so it does not exceed the baseline for a fully covered schema.

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

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

The description clearly states it estimates token count for a text string using the cl100k_base approximation. It distinguishes itself from siblings like estimate_llm_cost by focusing on token counting rather than cost, and mentions the return fields (token estimate, character count, word count) which further clarifies its function.

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

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

The description explicitly instructs to call it before sending text to an LLM API to check context window fit and estimate cost. This provides clear when-to-use context, but it does not mention alternative tools (e.g., truncate_to_tokens) or exclusions, so it lacks explicit alternatives.

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

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

Annotations already establish state-changing behavior via readOnlyHint=false and idempotentHint=false. The description adds value by disclosing that the page is structured with code blocks and tables, and reiterates the stateful nature. This goes beyond what annotations provide, although no additional warnings about duplicates or auth are included.

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

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

The description is two sentences, front-loaded with the core action and source, followed by formatting and statefulness. Every word earns its place; no redundancy or filler.

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

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

Given the tool's complexity (9 parameters, nested objects, output schema present), the description provides essential workflow context, source relationship, and formatting expectations. The output schema covers return details, so the description is sufficient for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, with each parameter already having a meaningful description (e.g., test_suite defined as 'from jira_to_test_suite result'). The tool description does not add new parameter details beyond the schema, so it meets the baseline but doesn't exceed it.

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

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

The description clearly states the action ('Create a new Confluence page') and the specific source ('output of jira_to_test_suite'), distinguishing it from sibling tools like fetch_confluence_page. It also details the content formatting (Gherkin, E2E steps, API tests) 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.

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

The description implies when to use it: after jira_to_test_suite produces its output. It does not explicitly mention alternatives or exclusions, but the 'from the output of jira_to_test_suite' phrase provides clear context. The STATEFUL warning adds a caution about side effects, which is useful for decision-making.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by specifying support for standard 5-field cron (minute hour day month weekday), which is useful beyond the annotations. It does not cover error behavior, but the output schema likely covers that.

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

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

Two sentences, front-loaded with the primary purpose, and no wasted words. Every part of the description contributes useful information.

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

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

The tool is simple with one parameter and an output schema present. The description is sufficiently complete for an agent to understand when and how to use it, and the format clarification covers the key edge case.

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

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

Schema coverage is 100% with a parameter description and examples. The description adds semantic value by explaining the exact format and field order, which goes beyond the schema's simple 'Cron expression' label.

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

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

The description clearly states a specific action ('Parse a cron expression') and a clear outcome ('into a human-readable schedule description'). It distinguishes itself from the sibling cron_validator by focusing on producing a description rather than validating.

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

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

The description implies the tool is for getting human-readable schedules from cron expressions, which gives clear context. However, it does not explicitly mention alternatives or when not to use it (e.g., if validation is needed, use cron_validator), so it misses the explicit exclusion criterion for a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by stating that it returns parsed fields, a human-readable description, and ISO timestamps, which is beyond annotation coverage and sets proper expectations for the output behavior.

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

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

Two sentences, front-loaded with the primary action, a clear use case, and a concise output summary. Every sentence earns its place with no redundancy.

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

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

With two well-described parameters, an output schema, and strong annotations, the description fully covers what an agent needs: purpose, usage, and return value highlights. The mention of ISO timestamps helps set format expectations, making this complete for the tool's complexity.

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

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

Schema description coverage is 100%, so the parameters are already well-documented. The description refers to '5-field cron expression' and 'upcoming ISO timestamps,' which aligns with schema but adds no new parameter semantics beyond what is already present.

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

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

The description states a specific verb ('Validate', 'explain', 'preview') applied to a clear resource ('5-field cron expression'), and it distinguishes itself from sibling cron_parse by adding validation and preview functionality. This is precise and unambiguous.

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

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

It explicitly gives a usage context: 'Use this to debug cron jobs before they reach production.' It doesn't mention when not to use it or name alternatives, but the use case is clear enough to guide an agent toward appropriate selection.

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

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

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds crucial behavior beyond annotations: it explicitly states the signature is NOT verified, which is a key security-relevant trait. It also mentions expiry reporting. This adds meaningful context without redundancy.

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

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

The description is two sentences, front-loaded with the core function. Every sentence adds value: the first states what it does, the second gives usage guidance. No fluff or repetition.

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

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

The tool is simple with one parameter and an output schema, so the description does not need to explain return values. It covers the purpose, key behavior (no signature verification), and typical use cases, making it fully sufficient for an agent to select and invoke the tool correctly.

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

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

The schema covers 100% of the parameter, including a description of the JWT format. The description does not add extra parameter-specific details beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description uses a specific verb ('Decode') and resource ('JWT'), clearly stating it returns header and payload without verifying the signature. It distinguishes itself from siblings like base64_decode by focusing specifically on JWT structure and expiry reporting. The scope is unambiguous.

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

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

The description provides a clear use case: 'Use to inspect claims (sub, iss, exp, roles) during debugging or when integrating with an auth provider.' This gives context for when to use the tool, though it does not explicitly mention exclusions or alternatives, which prevents a top score.

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

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context beyond that: it discloses the underlying method (n-gram frequency analysis and common word markers) and the supported language list, which sets expectations for accuracy and scope without contradicting annotations.

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

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

The description is two sentences, front-loads the purpose, and contains no filler. It efficiently provides the method and supported language list, earning every sentence's place.

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

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

For a simple single-parameter tool with an output schema, the description is fully complete: it explains what it does, how it works, and its language scope. The schema covers the input constraint, and annotations cover safety, so no critical information is missing.

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

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

The input schema fully documents the 'input' parameter and its minimum length requirement, so the description does not need to repeat it. The description adds no additional parameter semantics beyond what the schema already provides, aligning with the baseline for high schema coverage.

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

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

The description clearly states a specific verb ('Detect') and resource ('natural language of a text'), and distinguishes itself from sibling tools like detect_secrets or toxicity_scan by specifying its unique task. It also lists supported languages, further clarifying scope.

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

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

The description implies usage by describing what it does, but does not explicitly state when to use this tool versus alternatives or mention any exclusions. There is no guidance on when not to use it or how it differs from related text analysis tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds contextual behavior: it returns findings with severity and highlights what types of secrets are detected. This goes beyond the annotations, revealing the tool's output characteristics without contradicting the declared safety profile.

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

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

The description is three sentences: the first defines the core action and scope, the second states the return value, and the third gives usage guidance. It is front-loaded, informative, and contains no filler words.

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

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

Given the presence of a complete input schema (100% coverage) and an output schema, the description covers the tool's purpose, target content, return behavior, and usage context. It is fully adequate for an agent to select and invoke this tool correctly.

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

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

Schema coverage is 100%, so the parameters 'input' and 'filename' are fully described in the schema. The description does not provide additional parameter-level details beyond implying that 'input' contains code/config content. Baseline 3 is appropriate.

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

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

The description explicitly states the tool scans code/config files for hardcoded secrets and provides a comprehensive list of secret types (AWS keys, GitHub tokens, API keys, etc.). It uses a specific verb 'scan' and resource, making the purpose unambiguous and distinguishable from generic scanning tools.

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

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

The description gives a clear usage directive: 'Run before every commit.' This provides strong contextual guidance, though it does not explicitly mention alternatives or when not to use the tool. The lack of exclusions makes it a 4 rather than a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, and the description adds 'Pure and deterministic' and explains the verdict and drift outputs. No contradictions; the extra context about output categories enhances transparency.

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

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

The description is three dense sentences, front-loaded with the core purpose and output, followed by practical acquisition instructions. Every sentence earns its place without fluff.

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

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

Given the rich output schema and annotations, the description still adds necessary context about how to capture input mappings and the deterministic nature. It's complete for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% for the two required params, and the description adds meaningful detail beyond the schema by specifying the internal structure of 'elements' arrays and the optional 'context' field. This enriches understanding without redundancy.

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

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

The description clearly states the tool diffs a baseline page mapping against a current one and returns a CI-style verdict with per-element drift categories. This specific verb+resource combination distinguishes it from sibling diff tools like diff_text or json_diff.

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

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

The description provides clear context for when to use the tool (comparing baseline vs current page mappings) and how to obtain the required inputs via the companion package or browser snippet. It doesn't explicitly mention alternatives or exclusions, but the use case is evident.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds the LCS algorithm detail and specifies the return format (added/removed/unchanged counts and formatted hunks with configurable context lines), enriching behavioral understanding beyond annotations.

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

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

Two sentences, front-loaded with the core operation, then output details, then usage guidance. Every sentence contributes meaningful information with no redundancy or filler.

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

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

For a straightforward 3-parameter tool with full schema coverage and an output schema, the description covers purpose, output, and usage sufficiently. Nothing critical is missing; the LCS algorithm mention and return value summary make it complete.

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

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

Schema coverage is 100% with descriptive parameter definitions. The description reinforces the context line range but does not add substantial new semantic detail beyond what the schema already provides, matching the baseline for high schema coverage.

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

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

States a specific verb+resource: 'Compute a unified line-by-line diff between two text strings (LCS algorithm).' This clearly distinguishes it from sibling tools like json_diff, diff_mappings, or levenshtein_distance by focusing on text diffing with line-level granularity.

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

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

Gives explicit use cases: 'Use to compare versions of prompts, configs, code snippets, or any text where you need to see exactly what changed.' This provides clear context, though it doesn't explicitly mention alternatives or when not to use, so it stops short of a 5.

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

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

Adds useful context beyond annotations: runs entirely in-process, no API key, and the limitation that it's not real embeddings. However, the claim about 'pipe-separated pairs' in batch mode is inconsistent with the schema's array-of-objects definition, creating a minor ambiguity.

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

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

Four sentences deliver substantial information without redundancy. The core function is front-loaded, and the caveat about real embeddings is essential context. Slightly dense but all sentences earn their place.

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

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

With an output schema present, the description adequately covers purpose, limitations, alternatives, and use cases. The only notable gap is the inconsistent batch-mode format description, which could confuse an agent about how to pass input.

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

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

The schema already covers all parameters (100% coverage), so the description's mapping of 'bow', 'tfidf', 'ngram' to human-readable algorithm names adds value. The batch-mode explanation is helpful but the 'pipe-separated pairs' phrase conflicts with the schema's batch structure, slightly reducing clarity.

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

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

The description states a specific verb+resource ('Compute text similarity') and enumerates the exact local algorithms (Bag of Words, TF-IDF, Character N-grams). It explicitly distinguishes from true embeddings by clarifying 'NOT real embeddings' and pointing to run_semantic_tests, which differentiates it from sibling tools.

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

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

Provides explicit contrast with run_semantic_tests for embedding-based similarity, noting no API key needed and in-process execution. Also lists concrete use cases (RAG retrieval testing, semantic search evaluation, text deduplication), making when-to-use clear.

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

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

Annotations already declare read-only and idempotent behavior. The description supplements this with concrete parsing behaviors: quoted values, inline comments, export prefix, and escaped sequences, which adds useful context 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.

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

Three sentences: first states purpose, second details parsing capabilities, third gives use cases. Compact, front-loaded, and every sentence adds value.

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

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

Given a single parameter, complete annotations, and an output schema, the description covers all necessary context: what it does, how it handles edge cases, and when to use it. No gaps remain.

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

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

Schema coverage is 100% and the input parameter is well-described in the schema ('.env file content to parse (e.g. the output of cat .env)'). The description adds little beyond restating that it parses file content, so baseline 3 is appropriate.

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

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

Description clearly states the verb (Parse) and resource (.env file content), and specifies the output (JSON object). It distinguishes itself from sibling tools like parse_csv and parse_http_headers by focusing on dotenv parsing.

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

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

Explicitly lists target contexts: 'CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically.' While it doesn't mention when not to use it or alternatives, the provided contexts offer clear usage guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds the specific characters being escaped and the XSS-prevention purpose, going beyond the structural hints without contradicting them.

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

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

Two sentences, front-loaded with the core operation. Every word earns its place—lists exact characters, states the mandatory usage, and explains the security benefit without fluff.

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

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

For a simple one-parameter tool with an output schema and clear annotations, the description fully covers purpose, usage, and effect. No gaps are evident; it's complete and actionable on its own.

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

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

Schema coverage is 100% (input described as 'String to HTML-escape'). The description adds meaning by specifying which characters get escaped and preemptively addressing the security context, enhancing the schema's basic parameter description.

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

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

The description uses a specific verb ('Escape') and resource ('HTML special characters') listing the exact characters (&, <, >, ", '). It clearly distinguishes the tool from its sibling 'unescape_html' and other escaping utilities.

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

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

The description says 'ALWAYS call this before inserting any user-provided or LLM-generated content into an HTML template to prevent XSS attacks', which is explicit about when to use. It does not mention when not to use or alternative tools, but the strong 'ALWAYS' directive provides clear context.

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

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds useful context by listing the exact supported model families and that the result is a USD cost figure. It doesn't disclose edge cases like currency rounding or pricing data freshness, but the annotation coverage lowers the burden.

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

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

Two sentences, front-loaded with the core purpose and followed by a concise support list. No filler or repetition.

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

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

With an output schema present and simple inputs, the description covers the essential context: what is calculated, the currency, and the list of supported models. It could mention the source/timing of pricing data, but it is sufficient for selecting this tool among many siblings.

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

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

Schema coverage is 100% and all parameters are described with meaningful examples. The description adds only the general 'token counts' phrasing and the model list, which does not materially improve on the schema.

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

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

The description uses a specific verb ('Estimate') and resource ('API cost in USD') and states the inputs (model and token counts). It is clear and distinct from siblings like count_tokens, though it does not explicitly differentiate from token_budget_calculator.

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

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

The intended use is implied: call this when you need a cost estimate for a given model and token counts. However, there is no explicit guidance on when not to use it or which sibling tool might be preferable (e.g., model_info, count_tokens).

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds behavioral context beyond annotations by specifying that it extracts the *first* valid JSON and handles markdown-fenced and inline JSON, which is important for invocation. No contradiction with annotations found.

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

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

The description is concise and well-structured: the first sentence states the core function, the second covers input format handling, and the third gives explicit usage context. Every sentence earns its place with no redundancy or unnecessary detail.

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

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

For a tool with one parameter, complete schema coverage, an output schema, and thorough annotations, the description provides ample context. It covers what the tool does, when to use it, and what input formats are acceptable, making it easy for an agent to select and invoke the tool correctly. The minor absence of edge-case behavior (e.g., no JSON found) is negligible given the output schema.

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

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

The single parameter 'input' is well-defined in the schema, with high coverage. The description enriches the parameter meaning by clarifying the input type ('chaotic LLM output', 'surrounded by markdown fences, prose, or explanatory text') and that the extraction targets the first valid JSON—details that go beyond the schema description.

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

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

The description clearly states the tool's function: extracting the first valid JSON object/array from chaotic LLM output. It specifies the resource (JSON in text) and the action (extract), and distinguishes itself from sibling tools like extract_json_path and format_json by focusing on embedded data surrounded by prose or markdown fences.

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

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

The description provides an explicit when-to-use scenario: 'Call this whenever an LLM returns structured data mixed with explanation text instead of raw JSON.' This gives clear practical guidance. However, it does not name alternatives or explicitly state when not to use the tool, so it misses the upper boundary of a 5.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral details about path syntax and array access, which is useful beyond annotations. It does not discuss error cases, but output schema likely covers return behavior.

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

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

Two brief, front-loaded sentences with concrete examples. Every clause adds value, and the description is appropriately sized for a simple utility.

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

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

Given the tool's low complexity, full parameter documentation, existing output schema, and comprehensive annotations, the description fully covers the necessary context. No missing information that would impede correct tool invocation.

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

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

The input schema already has 100% parameter description coverage, so the baseline is 3. The description's examples and mention of array index access largely replicate what the schema already provides for the 'path' parameter, adding little new semantic value.

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

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

The description clearly states the function: extract a value from a JSON string using a dot-notation path, with concrete examples. It distinguishes this tool from siblings like extract_json_from_text or flatten_json by specifying the path-based traversal mechanism.

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

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

The description provides clear context on when to use the tool (extracting values via dot-notation paths, including array indexing) but does not explicitly mention when not to use it or name alternative tools. This meets the 'clear context, no exclusions' level.

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

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

Annotations already declare read-only and idempotent behavior. The description adds useful behavioral details: results are categorized and deduplicated. This goes beyond the annotation baseline without contradicting it.

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

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

The description is two sentences, front-loaded with the core function, and every sentence contributes meaning. No redundant words or structural clutter.

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

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

For a simple tool with full schema coverage, an output schema, and strong annotations, the description sufficiently covers purpose, behavior, and use cases. There are no critical gaps for an agent to use it correctly.

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

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

Both parameters are fully described in the schema (100% coverage), so the description does not need to add parameter details. It mentions 'all URLs, email addresses, and domain names', which aligns with the types enum but adds no extra semantic value beyond the schema.

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

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

The description clearly states the tool extracts URLs, email addresses, and domain names from text, with a specific verb ('Extract') and resource. It distinguishes itself from sibling tools like extract_todos or extract_json by focusing on link-related entities.

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

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

The description explicitly lists use cases: content auditing, link checking, and web scraping validation. This provides clear context for when to use the tool, though it does not mention exclusions or alternatives, which would push it to a 5.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds useful behavioral context by listing the output fields and the tag categories handled. This is consistent with annotations; no contradiction exists.

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

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

The description is two concise sentences with the action and scope front-loaded. It avoids redundancy and every phrase carries meaningful information, making it easy to parse quickly.

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

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

This is a simple, non-destructive tool with full schema coverage, a read-only annotation set, and an output schema. The description adds the essential purpose and a clear use case, so the agent has everything needed to select and invoke the tool correctly without ambiguity.

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

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

The input schema already provides descriptions for all three parameters (100% coverage), so the schema carries the semantic load. The description mentions custom tags generically but doesn't add new details about the tags, include_context, or input parameter beyond what the schema already states. Baseline 3 is appropriate.

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

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

The description uses a specific verb ('extract'), lists concrete tag types (TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE), and states the return contents (line numbers, tag types, message text). This makes the tool's purpose unmistakable and differentiates it from sibling extractors like extract_links or extract_json.

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

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

The description clearly identifies a primary use case ('technical debt auditing') and says it works on 'any source code or text', giving the agent context for when to select this tool. However, it does not name alternative tools or specify when not to use it, so it stops short of explicit when/when-not guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to repeat that. It adds valuable behavioral context: credentials transit in-memory only and are never stored, output is clean Markdown, and the tool can optionally list child pages. This exceeds the annotation baseline.

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

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

The description is two sentences long, front-loaded with the core purpose, and every clause adds value: input forms, optional behavior, and credential handling. No irrelevant details or repetition.

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

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

Given that an output schema exists, the description doesn't need to detail return values. It covers the essential invocation details (page_id/URL, credentials, optional children) and auth handling. The description is complete enough for an agent to use the tool safely and correctly.

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

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

The input schema has 100% coverage with detailed descriptions for every parameter, including the alternative relationship between page_id and page_url. The tool description adds little beyond restating those facts, though it does clarify 'optionality' of children listing. Since the schema is rich, the description earns the baseline score but no more.

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

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

The description uses a specific verb 'Fetch' with a clear resource 'Confluence page' and specifies the output format 'clean Markdown'. It clearly distinguishes this from sibling tools like 'create_confluence_page' and 'fetch_jira_issue' by naming the exact system and action.

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

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

The description states when to use the tool (to fetch a Confluence page) and mentions two input options (page_id or page_url) plus an optional children list. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it over siblings.

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

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that the data comes from curated RSS sources and is aggregated, and that it fetches 'latest' content, which implies dynamic updates. This is useful context beyond the annotations, though it doesn't mention network dependency or failure modes.

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

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

The description is two sentences long, front-loaded with the primary function and sources, followed by a single sentence stating its purpose. No wasted words; every sentence contributes to understanding.

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

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

The tool is simple with an output schema present, so the description needn't detail return values. It covers what (articles), from where (specific RSS sources), and why (monitoring QA & AI landscape). It could mention potential network failures, but that is a minor gap.

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

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

Schema description coverage is 100%, so the parameters (limit, category) are already fully documented. The tool description does not mention the parameters or add any additional meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description states a specific verb ('Fetch') and resource ('latest QA & AI/LLM articles aggregated from curated RSS sources') and lists the exact sources (Google Testing Blog, DEV.to, Hugging Face Blog, Simon Willison). This clearly distinguishes it from sibling fetch tools like fetch_confluence_page or fetch_jira_issue.

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

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

The phrase 'Perfect for agents monitoring the QA & AI landscape' provides clear context for when to use this tool. It doesn't explicitly exclude alternative tools or mention when not to use it, but the use case is well-defined.

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

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool's safe, non-mutating nature is well established. The description adds that it converts example pairs into formatted blocks, which is largely synonymous with its purpose and provides minimal extra behavioral context 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.

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

The description is concise, with two sentences that are front-loaded with the core purpose. It lists the supported formats without wasting words, making it easy to scan and understand.

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

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

The description covers the tool's main inputs and outputs, and the schema and annotations provide the rest. Since the tool is relatively simple and has high schema coverage, the description is sufficiently complete for an agent to use it correctly, though it could theoretically include an example of the output format.

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

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

The input schema has 100% coverage, with descriptions for all four parameters including enum values for format. The description only repeats the format options and mentions example pairs, adding little beyond the schema. Per the baseline for high schema coverage, this is adequate but not exceptional.

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

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

The description clearly states the tool's purpose: formatting few-shot examples for LLM prompts. It specifies the action (format/converts), the resource (few-shot examples), and the supported output formats (chat, XML, Markdown, plain text), which distinguishes it from sibling formatting tools.

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

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

The description implies usage context by stating it formats few-shot examples for LLM prompts, which is clear enough for most users. However, it doesn't explicitly mention alternatives or when not to use this tool, but the context is sufficient given the sibling list.

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

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description adds critical behavioral context: it calls an external LLM, uses 300-500 tokens, requires a BYOK key, and never stores the key. It also clarifies it only fixes flagged issues, providing context about the side effects and dependencies that annotations do not cover.

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

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

The description is three sentences: the first states the purpose, the second explains inputs/process/output, and the third adds cost and key requirements. Every sentence earns its place, and the key information is front-loaded. No redundancy.

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

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

Given the tool's complexity (external LLM call, 4 params, output schema), the description covers the input source, process, output, token cost, and BYOK requirement. The output schema exists, so not detailing the return structure is fine. It is complete enough for an agent to invoke correctly, though it omits nuanced edge-case behavior.

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

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

Schema coverage is 100%, so baseline 3 applies. The description adds meaning by linking 'gherkin' to 'test_suite.gherkin' and 'warnings' to '_gherkin_warnings', and by clarifying the fix scope ('ONLY the flagged issues') with an example ('adds missing Given/When/Then steps'). This enriches the parameter roles beyond the schema descriptions.

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

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

The description starts with 'Fix Gherkin syntax warnings from a jira_to_test_suite result', which is a specific verb+resource+source combination. It clearly distinguishes itself from sibling tools like jira_to_test_suite (generation) and generic LLM tools by focusing on post-hoc fixing of flagged warnings.

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

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

It identifies when to use the tool: when there are _gherkin_warnings from a jira_to_test_suite result. The cost comparison '~300-500 tokens vs ~5k for a full regeneration' implies using this lightweight fix instead of regenerating the whole suite. The schema adds explicit 'do not call if you have no key', but the main description gives solid context without naming a specific alternative tool.

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

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

Annotations already establish this as a safe, read-only, idempotent operation. The description adds behavioral specifics (dual mode, dot-notation, custom separators) without contradicting annotations, and for a pure function 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.