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 readOnlyHint=true and destructiveHint=false. Description adds output details (statistical metrics) but no additional behavioral traits. No contradiction. With annotations, bar is lower and description is adequate.

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

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

Two sentences, front-loaded with main action, no filler. Every word 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 output schema present, description needn't detail return values. Covers input (arrays of scores) and output (statistics) adequately for a statistical tool. No gaps.

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 property descriptions. Description reiterates that scores are arrays and mentions statistical output, but does not add meaning beyond 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?

Description uses specific verb 'Generate' and resource 'A/B test report', clearly states it compares two prompts/model configurations, and lists outputs. Distinguishes from siblings like 'compare_models' by focusing on statistical report with specific metrics.

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?

Description implies use case (when you need A/B test comparison), but provides no explicit guidelines on when to use or avoid, no mention of prerequisites or alternatives. Minimum viable.

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 provide readOnlyHint=true, destructiveHint=false, so the description does not need to cover safety. It adds that the tool returns issues with severity levels and test suggestions, but does not disclose additional behavioral traits like error handling or limits, providing moderate 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 a single, front-loaded sentence that efficiently conveys the tool's purpose and output without any extraneous text, earning a top score.

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 with 3 parameters and existing output schema, the description covers the core functionality and return value. It lacks details on supported languages or input formats, but is sufficiently complete for an agent with good schema and annotations.

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 all parameters. The description adds context that the tool works with a git diff or two versions, but does not enhance parameter semantics beyond the schema, meeting the baseline.

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 'Detect potential bugs and code smells from a git diff or two code versions', providing a specific verb and resource. It distinguishes itself from sibling tools like secret_scan or bias_detect by focusing on code diffs and returning issues with severity and test suggestions.

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 review but does not explicitly state when to use this over other tools or when not to use it. No alternatives or exclusions are mentioned, relying on the agent to infer context from the purpose.

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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds 'Deterministic, no LLM, no key — gate-able in CI', providing additional behavioral context consistent 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?

Description is front-loaded with purpose, then conditional logic, then usage guidance. Each sentence adds value, though slightly dense.

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?

Covers main behavior: consensus, pairwise cosine agreement, most representative, outlier, and with reference ranking. Output schema exists, so return values are not required. Could mention number of outputs min but schema covers it.

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%, baseline 3. Description adds meaning by explaining the optional 'reference' as ground truth and the 'responses' structure, and how behavior changes with/without reference.

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 'Semantically analyze N already-produced model outputs for the SAME task' with clear verb and resource. It distinguishes from sibling 'compare_responses' by specifying that tool is for 2-way head-to-head with structural 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?

Explicitly tells when to use with/without a reference. States 'Deterministic, no LLM, no key — gate-able in CI' for context. Clearly directs to use 'compare_responses' instead for 2-way head-to-head JSON diff.

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 declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it decodes to UTF-8 text, which is consistent and adds output format context 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?

The description is a single sentence with examples, front-loaded with the core action. Every part adds value; 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 low complexity and the existence of an output schema, the description is complete. It covers what the tool does, when to use it, and the output format without needing extra details.

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 and describes the single parameter clearly ('Base64 string to decode'). The description does not add further meaning beyond what the schema already provides, so baseline 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 it decodes Base64 to UTF-8 text. It gives specific use cases (API responses, JWT payloads, config files, attachments) and distinguishes from sibling tool base64_encode by specifying decode vs 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 provides explicit use cases (inspecting API responses, JWT claims, config values, attachment data) but does not indicate when not to use it or mention alternatives beyond the sibling encode 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 indicate readOnly, idempotent, non-destructive. The description adds that the input is UTF-8 and the output is Base64, which are useful behavioral traits. 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 concise, front-loaded sentences that cover purpose and usage without superfluous words. 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?

For a simple tool with one parameter and clear annotations, the description is complete. It explains the input type, output format, and appropriate use cases, fully addressing the tool's purpose.

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 description adds only that the input is UTF-8, which marginally improves understanding. Baseline 3 is appropriate as the schema already describes the parameter sufficiently.

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 ('Encode a UTF-8 string to Base64') and distinguishes it from the sibling 'base64_decode'. The verb 'encode' and resource 'UTF-8 string to Base64' are specific and unique.

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 explicit use cases ('embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data URIs'), guiding when to use. It lacks explicit when-not-to-use or alternative mentions, but the 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?

Annotations indicate read-only, idempotent, non-destructive. Description adds input requirements (same prompt template, demographic variants) and output details (score, sentiment, similarity, verdict). 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?

Two sentences, no redundancy, front-loaded with action and context.

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 annotations and schema, the description adequately covers input, processing, output, and runtime behavior (local).

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?

Only one parameter with 100% schema coverage. Description adds context about demographic variants (gender, origin, age, tone) beyond schema's variantId 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?

Clearly states verb 'Analyse', resource 'set of LLM responses', and specifies demographic variants. Distinct from sibling tools like compare_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?

Implicitly for bias detection, notes local execution (no API key). Lacks explicit when-not-to-use or alternatives.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it returns ranked results with normalized scores, providing useful 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 consists of two concise sentences that efficiently convey the tool's purpose and industry relevance without unnecessary 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?

Given full schema coverage and annotations, the description adequately explains the tool's function and return format. It covers what BM25 is and its typical use, making it complete for this compute-oriented 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 coverage is 100% with parameter descriptions. The description does not add specific parameter details but provides algorithm context that aids understanding of how query and documents are used.

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 computes BM25 relevance scores between a query and documents, and distinguishes it from sibling tools by specifying it's a keyword-based algorithm used in Elasticsearch, OpenSearch, and Weaviate hybrid search.

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 labeling BM25 as a keyword-based algorithm, implying it's for keyword matching rather than semantic similarity. However, it does not explicitly exclude alternative use cases or mention when not to use it.

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 (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already cover safety. The description adds meaningful behavioral details like token budgeting, citation numbering, and system instruction injection, enhancing transparency 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?

The description is a single, well-structured sentence (20 words) that front-loads the main action and efficiently lists key features. Every part 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?

Given 6 parameters, 2 required, and an output schema, the description covers the main aspects: token budgeting, citation numbering, system instruction, language, and source attribution. It doesn't explain return values (covered by output schema) and assumes query/chunks are self-explanatory, so it's nearly 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 description coverage is 100%, so baseline is 3. The description briefly mentions token budgeting, citations, and system instructions, but does not add specific parameter semantics beyond what the schema already provides (e.g., 'max_context_tokens' is already described).

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 ('assemble') and resource ('RAG prompt'), and lists distinct features (token budgeting, citation numbering, system instruction injection, source attribution). This clearly distinguishes it from sibling tools like 'prompt_template_fill' 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?

The description implies use after retrieval for building a final prompt, but does not explicitly state when to use or avoid this tool, nor does it mention alternatives. Guidance is somewhat implicit, so it scores as adequate but not explicit.

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 the agent knows this is a safe, deterministic operation. The description adds no additional behavioral context beyond what the annotations provide, e.g., behavior on empty text or edge cases.

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, efficient and front-loaded. It states the tool's output in the first sentence and a primary use case in the second, with no redundant or irrelevant content.

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 simplicity (single parameter, read-only, output schema exists), the description adequately covers the necessary information. It could be improved by mentioning supported text characteristics (e.g., language), but it is sufficient 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?

The input schema covers 100% of parameters with a description for the single 'input' field. The main description does not add any new meaning beyond the schema, so baseline score 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 verb 'Calculate' and the resource 'readability scores', listing four specific indexes. It clearly distinguishes this tool from siblings, as none of the 150+ sibling tools are related to readability.

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 mentions 'useful for evaluating LLM output quality' as a usage context, but does not provide explicit when-to-use or when-not-to-use guidance, nor alternatives. The usage is implied rather than explicit.

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 readOnly, idempotent, non-destructive. Description adds that it converts strings, reinforcing stateless transformation. 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?

Two sentences, front-loaded with core functionality and list of cases. No extraneous 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?

Complete for a simple conversion tool with output schema. No missing context like prerequisites or side effects.

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 clear parameter descriptions. The tool description lists naming conventions already present in schema, adding no new semantic depth.

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?

Clearly states 'Convert a string between naming conventions' and lists all supported cases. Distinguishes from siblings (no other case converter in list).

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?

Mentions 'Essential for code generation and refactoring' providing context. Does not explicitly state when not to use or compare with 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 read-only, idempotent, non-destructive behavior. The description adds that it returns compliance levels but lacks further behavioral context such as edge cases or input validation.

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?

A single sentence that is front-loaded and contains all necessary information without waste. 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?

Given strong annotations and an output schema, the description adequately covers the tool's purpose and return value. It could mention output format explicitly but is sufficient for agent selection.

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 add additional semantic meaning beyond referencing the two colors. It does not specify hex format details already present in 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 calculates WCAG 2.1 contrast ratio between two colors and returns ratio with compliance for AA/AAA levels. It distinguishes itself from sibling tools like color_convert and calculate_readability.

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 contrast checking but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use scenarios.

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's 'Convert' is consistent. Adds accepted format details but doesn't disclose additional behavior like error handling or output structure beyond 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?

Four concise sentences with immediate action verb, front-loaded with purpose, then usage examples and format details. No 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 conversion tool with one parameter and an output schema (exists but not shown), the description covers input formats and use cases fully.

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 basic 'Color value to convert' description. The tool description adds specific format examples (#rrggbb, rgb(r,g,b), hsl(h,s%,l%)) that clarify valid input 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 'Convert a color between HEX, RGB, and HSL formats' with specific verbs and resources, and distinguishes from sibling conversion tools (e.g., base64_decode, case_convert) by focusing on color 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?

Provides explicit when-to-use examples: 'translating design tokens', 'verifying color accessibility', 'normalizing color values'. Lacks explicit when-not-to-use but context is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that it returns a comparison table with recommendation, providing useful output context 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?

Two sentences cover purpose, scope, and output. Front-loaded with key information. No unnecessary 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 schema and annotations, the description is complete: it specifies the action, input constraints (2-5 models), comparison dimensions, and output format. The presence of an output schema means return values need not be detailed.

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 descriptions for both parameters. The description adds the overall purpose but does not provide additional meaning beyond the schema (e.g., enum options of use_case are already listed). Baseline 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?

Description clearly states the verb 'Compare' and resource 'AI models side by side' with specific attributes (context window, pricing, multimodal, reasoning, provider). It distinguishes from siblings like 'compare_responses' and 'model_info' by focusing on cross-model comparison for selection.

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 comparing models but does not explicitly state when to use versus alternatives. Sibling tools like 'compare_responses' or 'model_info' exist, but no when-not guidance is provided.

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 fully discloses behavioral traits: it is read-only, idempotent, non-destructive, and returns deterministic metrics. It also explains the optional LLM judge behavior. These align with annotations and add value beyond 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 concise (4 sentences), front-loaded with the main purpose, and well-structured with clauses for optional features. Every sentence adds value without 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 complexity (9 parameters with optional features) and the existence of an output schema, the description covers all necessary information. It explains when and how to use each optional parameter, making the tool self-contained.

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 is 3. The description adds meaning by explaining the role of each optional parameter: 'task' is for judge context, 'reference' for scoring against ground truth, 'model'/'api_key' for LLM judge. This goes beyond the schema's basic 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 compares two already-produced outputs side by side, specifying it returns deterministic metrics and a verdict. It distinguishes itself from siblings like diff_text, similarity_score, and json_diff by emphasizing 'no re-execution' and listing specific metrics (token cosine, ROUGE-L, Jaccard, etc.).

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: for comparing two outputs with optional reference or LLM judge. It implicitly differentiates from raw text diff tools by focusing on outputs and metrics. However, it could explicitly state not to use this for simple text differencing or for re-executing tasks.

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 idempotent, read-only, non-destructive behavior. Description adds value by stating 'Fast, deterministic, no API key needed' and discloses the limitation of surface-level matching, which is crucial for proper usage.

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 with critical information front-loaded. No unnecessary words, and every sentence adds value: purpose, behavior, limitations, alternative.

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 has an output schema and full parameter coverage, the description sufficiently covers behavior, limitations, and usage context. No gaps.

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 with descriptions. Description adds no additional detail beyond what the schema provides, so 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 clearly states the tool's purpose: comparing multiple LLM responses for inconsistencies using Jaccard similarity and fact drift. It distinguishes itself from sibling tool 'run_semantic_tests' by noting its focus on surface-level matching.

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 advises when to use this tool (fast, deterministic, no API key) and when not to (for semantic consistency, use run_semantic_tests). Also clearly outlines limitations.

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. Description adds behavioral detail about warning on truncation risk, which is useful beyond annotations. 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?

Two sentences, front-loaded with purpose. Every sentence adds value 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?

Given moderate complexity (3 params, no enums, output schema exists), the description is complete enough. Could mention output structure but not required due to 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 coverage is 100% and parameters are well-described. Description does not add significant extra meaning beyond what schema provides, so baseline 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?

Description clearly states it estimates token usage and checks context window fit, with a specific warning about truncation risk. Differentiates from sibling tools like count_tokens by adding the context window 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?

Implies usage for token estimation and context check but does not explicitly state when to use vs alternatives (e.g., count_tokens) or provide exclusions. No guidance on when not to use.

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, indicating safety. The description adds specific behavioral traits: the tool analyzes four distinct dimensions (context retention, topic drift, instruction following, repetition) beyond what annotations provide, enhancing 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 two sentences: the first states the action and dimensions, the second specifies the input format and utility. Every sentence adds value, no redundancy, and information is front-loaded.

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 single required parameter and the presence of an output schema, the description covers all necessary context: what is analyzed (four aspects), input format, and common use case (chatbot QA). It is sufficiently complete for an agent to select and invoke 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 already describes the 'messages' parameter structure with enum roles and order, and schema coverage is 100%. The description restates this as 'messages array [{role, content}]' but adds no new semantic detail beyond reinforcing the conversational context. 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 specifies the tool's purpose: analyzing multi-turn conversations for context retention, topic drift, instruction following, and repetition. It names the resource (conversation) and action (analyze), and distinguishes from siblings like 'hallucination_check' and 'consistency_check' by focusing on comprehensive conversation analysis.

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 chatbot QA but does not explicitly state when to use this tool over alternatives like 'response_quality_score' or 'context_window_check'. It lacks exclusion criteria or alternative recommendations, leaving the agent to infer 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. The description adds detailed behavioral context: fetches the URL, inspects Set-Cookie headers, flags insecure patterns, and returns grades/score. This goes beyond 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 four sentences, front-loaded with the core purpose, then lists specific attributes and flags, and ends with output summary. No redundant information; every sentence is informative and 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 tool with one parameter, existing annotations, and output schema, the description thoroughly covers what the tool does, how it inspects, what it flags, and what it returns. It provides sufficient context for an AI 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 has 100% coverage with a clear description for the url parameter. The tool description further explains how the URL will be used (fetched for cookie inspection), adding meaning beyond the schema alone.

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 security attributes of cookies for any URL, specifies what it inspects (HttpOnly, Secure, SameSite, etc.), and what it returns (per-cookie grades and overall score). This distinguishes it from sibling tools like security_headers_check or ssl_certificate_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 implicitly tells when to use: when needing to audit cookie security for a URL. No explicit exclusions or alternatives are mentioned, but the context's sibling list shows no other cookie-specific tool, so the purpose 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context beyond annotations: it explains the tool simulates browser behavior and returns the preflight OPTIONS response, which is not indicated in 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 two sentences long, with the first sentence stating the tool's functionality and the second providing usage context. No unnecessary words 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 has an output schema (not shown), and the description explains the return values (status, headers, origin, preflight). Given the annotations and schema coverage, the description is adequate for understanding the tool's 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 description coverage is 100%, and the schema provides sufficient descriptions for all three parameters. The description does not add additional semantic or formatting details about parameters beyond what the schema already 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 clearly states the tool checks CORS configuration like a browser and returns specific response details. It distinguishes itself from the sibling 'cors_test' by specifying it is for direct CORS debugging, not just security auditing.

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 usage hint ('Use this for direct CORS debugging, not just security auditing') but does not explicitly state when not to use the tool or mention alternative tools like 'cors_test'. The guidance is implied rather than explicit.

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 (readOnlyHint=true, openWorldHint=true) indicate safety and external requests. Description adds specifics about sending preflight and cross-origin requests and the types of misconfigurations detected, providing useful context 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?

Three well-structured sentences that front-load purpose, list detection categories, and mention return value. 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 complexity and presence of output schema, description covers what the tool returns (risk level, per-test results, fix recommendations) and its purpose in security audits. Complete for an MCP 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 coverage is 100%, and description adds context about how parameters are used (e.g., 'sends preflight and cross-origin requests with various Origin headers'). The optional origin parameter is explained.

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 tests CORS misconfigurations and lists specific checks (wildcard origins, origin reflection, etc.). It distinguishes from siblings by focusing on security audits and providing detailed detection categories.

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 use for API security audits but does not explicitly state when not to use it or mention alternatives like cors_checker. However, 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?

Annotations already indicate read-only and idempotent behavior. The description adds value by detailing what the tool extracts (step count, logical flow, conclusion presence, etc.), which are not apparent from annotations alone. It does not contradict 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: first defines the tool's purpose and capabilities, second gives concrete usage context. No redundant words, and critical information is front-loaded. This is an example of efficient, well-structured description.

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 has only two required parameters and an output schema, the description covers inputs and analysis functions adequately. It does not describe the output format, but the presence of an output schema relieves that burden. Minor gap: no mention of handling long or malformed traces.

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 is 3. The description does not enhance parameter explanations beyond what the schema provides (e.g., details on expected_conclusion format or behavior). The extra analysis capabilities mentioned are not tied to specific 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 explicitly states the tool analyzes Chain-of-Thought reasoning traces, listing specific detection capabilities like step count, logical flow, and backtracking. This clearly distinguishes it from sibling tools such as bias_detect or hallucination_check, which focus on other aspects of LLM output.

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 mentions it is 'useful for o1/o3/DeepSeek-R1 evaluation', providing clear context for when to use it. However, it does not advise against using it in other scenarios or name alternatives, such as conversation_analyze for full conversations.

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, destructiveHint=false, so the description adds no further behavioral context beyond listing supported languages. No contradictions. The bar is lowered by annotations, and the description meets it minimally.

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 fluff. The first sentence enumerates the outputs, and the second lists supported languages. Every sentence is informative and 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 an output schema, so return values are covered. The description is complete for a simple counting tool, though it could mention if it handles file extensions or comment styles for each language. Still, it's adequate.

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%: both 'input' and 'language' have descriptions. The description adds value by listing the exact language codes, but this is already implied by the schema's language hint. 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 counts lines of code with specific breakdowns (total, code, comment, blank lines, density) and lists supported languages. The name is self-explanatory, and it is distinct from sibling tools like text stats or analysis 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 does not explicitly state when to use this tool versus alternatives. It implies usage for code analysis but lacks guidance on context, exclusions, or when another tool might be better. Given the sibling set includes many text processing tools, this is a gap.

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, so safety profile is clear. The description adds valuable behavioral context: approximation method (cl100k_base, ~4 chars/token) and return values (token estimate, char count, word count). 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 plus an introductory line, all relevant and without redundancy. It efficiently covers purpose, usage, and output, 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?

For a simple tool with one string parameter and an output schema (present but not shown), the description sufficiently covers the estimation method, use case, and return values. No missing critical 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?

Schema description coverage is 100%, so baseline 3. The parameter 'input' is described in schema as 'Text to count tokens for', and the main description adds the encoding detail but not directly in the parameter context. No significant additional parameter-specific meaning.

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

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

The description clearly states it estimates token count of a text string using cl100k_base approximation, and distinguishes from siblings by specifying the method and output fields. The verb 'estimate' and resource 'token count' are specific.

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 to call this before sending text to an LLM API to check context window fit and estimate cost, providing clear when-to-use guidance. However, it does not explicitly exclude other tools or mention alternatives in the sibling set, slightly reducing differentiation.

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 value beyond annotations by describing the formatting behavior (code blocks, tables) and emphasizing statefulness. Annotations are all false, so no contradictions; description fills in behavioral details.

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 focused sentences with no wasted words. Front-loaded with verb and resource, followed by specific details and statefulness warning.

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?

Covers core purpose and integration point. Could benefit from mentioning prerequisite steps or page structure, but output schema likely handles return details. Adequate for a creation tool with rich 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 coverage is 100%, baseline 3. Description links the test_suite parameter to jira_to_test_suite, adding semantic context. Does not repeat schema descriptions but provides integration guidance.

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?

Clearly states 'Create a new Confluence page' with specific content formatting (Gherkin, E2E steps, API tests). Distinguishes from sibling tools like fetch_confluence_page (read) and jira_to_test_suite (input generation).

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 mentions it takes output from jira_to_test_suite as input, establishing a prerequisite. Also notes statefulness. Lacks explicit when-not-to-use or alternative tools, but context is clear.

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

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 tool's safety profile is clear. The description adds the context that the output is a 'human-readable schedule description', which is useful but does not go beyond the annotations. There is 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?

The description is two sentences long, with no wasted words. It front-loads the primary action and resource, then adds the format constraint. Every sentence earns its place, and the structure is clear and efficient.

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 adequately complete. It explains what the tool does, what format it accepts, and hints at the output. It does not mention error handling or unsupported cron variants, but the simplicity and the presence of an output schema make additional detail less 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 input schema has 100% coverage for the required 'expression' parameter, with a description of example values. The description adds semantic value by explaining the supported format: 'standard 5-field cron (minute hour day month weekday)', which helps the agent understand valid expressions beyond the schema examples. This is meaningful context.

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: 'Parse a cron expression into a human-readable schedule description.' It specifies the verb 'parse' and the resource 'cron expression', and it distinguishes from sibling tools like cron_validator by not mentioning validation. The specification of 'standard 5-field cron' adds precision.

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 does not provide explicit guidance on when to use this tool versus alternatives such as cron_validator. It states that it supports standard 5-field cron but does not mention use cases, exclusions, or prerequisites. The usage context is implied but not direct.

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 behavioral details: it returns parsed fields, a human-readable description, and upcoming ISO timestamps. 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?

Two sentences with no wasted words. The first sentence explains the core functionality, the second gives usage context and output summary.

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 full schema coverage and an output schema, the description is complete. It provides usage context and output details, making it easy for an agent to decide when to use this 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 coverage is 100%, so the schema documents both parameters. The description includes an example cron expression but does not add significant semantic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool validates a 5-field cron expression, explains the schedule, and previews next execution times. It distinguishes from the sibling tool `cron_parse` by focusing on validation and debugging.

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 this to debug cron jobs before they reach production,' which provides a clear when-to-use context. It does not mention when not to use or alternatives, but the specificity 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?

The description adds behavioral context beyond annotations: it explains that signature verification is not performed and that the tool reports expiration status and date. This complements the readOnlyHint and idempotentHint annotations without 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?

Two precise sentences: first defines core functionality, second states use case. No extraneous content, front-loaded with key 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?

Given the presence of an output schema (not shown but indicated), the description fully covers what the tool returns (header, payload, expiration info) and its use case, 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?

With 100% schema coverage, the description adds minimal value over the schema's parameter description, only restating the token format. 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 clearly states the tool decodes a JWT, returns header and payload without verification, and reports expiration. It uses specific verb 'decode' and resource 'JWT', distinguishing it from siblings like base64_decode or hash_text.

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 advises using the tool to inspect claims during debugging or auth integration, providing clear context. However, it does not explicitly state when not to use it or mention alternatives, though no direct sibling exists.

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 discloses the analysis method (n-gram frequency analysis and common word markers) and the minimum character requirement, adding value beyond the annotations (which only indicate read-only, idempotent, non-destructive). It does not contradict annotations. However, it does not detail handling of out-of-list languages or short inputs.

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: the first explains the core function and method, the second lists supported languages and a usage hint. It is front-loaded with the action, no redundancy, and every sentence serves a 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 simplicity (one parameter, good annotations, output schema exists), the description covers purpose, method, language range, and a usage hint. It is fairly complete, though it could mention expected output format or error cases. The output schema likely covers return values.

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 single parameter 'input'. The description adds the 'min 20 chars for accuracy' detail, which is not in the schema description. This enhances understanding beyond the schema alone. Baseline 3 increased to 4 for the added 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 action ('Detect the natural language') and the resource ('a text'). It lists 15 supported languages, distinguishing it from sibling tools like 'calculate_readability' or 'text_stats'. The verb and resource are specific 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?

The description mentions a minimum character length (20 chars) for accuracy, which helps in usage, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., other text analysis tools). No 'when not to use' or alternative tool references are 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 and idempotent behavior. The description adds that it returns findings with severity, which complements the annotations without contradiction. It does not elaborate on all behaviors (e.g., output structure), but the output schema exists to cover 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?

The description is extremely concise: two sentences total. The first sentence introduces the action and key examples, and the second sentence mentions return format and usage recommendation. No redundant words or unnecessary 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 core purpose, return format (findings with severity), and usage recommendation. Given the presence of an output schema, it does not need to detail return values. It is complete enough for a scanning tool with two well-documented parameters.

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 'input' and 'filename' parameters are fully described in the input schema with 100% coverage. The tool description does not add new semantic information beyond the schema (e.g., 'input' is the content to scan, 'filename' provides context). Baseline score 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 scans code/config files for hardcoded secrets, listing specific types like AWS keys, GitHub tokens, and API keys. This verb+resource combination is highly specific and distinguishes it from generic sibling tools like 'secret_scan'.

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 includes the explicit recommendation 'Run before every commit,' which provides a clear usage context. However, it does not specify when not to use this tool or mention alternatives such as 'secret_scan,' leaving some ambiguity.

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 value by disclosing the algorithm (LCS), the output format (counts and diff hunks), and configurable context lines, providing useful 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 two concise sentences with no wasted words. It front-loads the main action and outcomes, making it easy to grasp 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?

Given the presence of an output schema that likely documents return values, the description sufficiently covers the tool's purpose, input parameters, output summary, and use cases. It is complete for a relatively simple diff 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 coverage is 100%, so the description adds no additional meaning beyond the schema. It mentions 'configurable context lines (0–20)' but does not elaborate on parameter usage or constraints beyond what the schema already 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 clearly states the tool computes a unified line-by-line diff between two text strings using LCS algorithm. It lists specific use cases (prompts, configs, code snippets) but does not explicitly differentiate it from sibling comparison tools like compare_responses 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 contexts for when to use the tool ('compare versions of prompts, configs, code snippets, or any text where you need to see exactly what changed') but does not specify when not to use it or mention alternative tools for similar tasks.

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 valuable behavioral context: it runs entirely in-process, requires no API key, and explicitly states it is not real embeddings. This goes beyond the annotations by explaining the computational model and limitations.

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

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

The description is concise at three sentences. The first sentence immediately states the core function and algorithms. The second adds the key distinctions (no API key, not real embeddings). The third lists use cases. Every sentence is purposeful 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?

Given the tool's simplicity (4 optional params, no required, output schema exists), the description covers all essential aspects: purpose, usage guidelines, behavioral transparency, and use cases. It does not need to describe return values because the output schema is present and handles that.

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 baseline is 3. The description adds context about single pair vs batch mode and mentions 'pipe-separated pairs', which is slightly misleading as the schema defines batch as an array of objects. However, the schema is authoritative, so the description adds marginal value beyond the schema's parameter 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 verb 'Compute', the resource 'text similarity', and specifies the local algorithms (Bag of Words, TF-IDF, Character N-grams). It distinguishes from semantic embedding tools like run_semantic_tests, providing a specific and distinct purpose.

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 explicit guidance on when to use (no API key needed, in-process) and when not to use (NOT real embeddings, alternative run_semantic_tests for true semantic similarity). It lists concrete use cases (RAG retrieval testing, semantic search evaluation, text deduplication), making the decision 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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, non-mutating operation. The description adds value by detailing handling of quotes, comments, export, and escape sequences, which goes 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 a clear first sentence stating purpose, followed by details and use cases. Every sentence adds value, and the structure is well front-loaded.

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 has a single parameter, high schema coverage, and an output schema, the description provides all necessary context: input format, parsing behavior, and expected output (JSON object). It is 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 coverage is 100%, and the parameter description is clear. The description adds extra context about parsing behavior and edge cases, improving parameter understanding 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 explicitly states the tool parses .env file content into a JSON object, listing supported features (quoted values, comments, export prefix, escapes). It clearly distinguishes from sibling tools, as no other tool in the list specifically parses .env files.

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 explicit use cases: CI/CD pipelines, agent config loaders, or processing dotenv files programmatically. It does not state when not to use, but the tool is sufficiently specialized that this is acceptable.

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 clear. The description adds that it escapes specific characters, which is consistent with annotations. No additional behavioral traits are needed beyond stating the transformation.

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 action and purpose. Every sentence adds value: first explains what it does, second gives explicit usage guidance. 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's simplicity (one parameter, output schema present), the description covers everything needed: the operation, the specific characters, and the critical security context. 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 parameter 'input' has a clear description. The tool description does not add new information about the parameter beyond what the schema provides, so baseline 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 clearly states the tool escapes HTML special characters to safe entities, specifying the exact characters affected. It also differentiates by emphasizing security (XSS prevention) and implies a clear use case, distinguishing it from siblings like 'unescape_html' or other encoding 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 explicit when-to-use guidance: 'ALWAYS call this before inserting any user-provided or LLM-generated content into an HTML template to prevent XSS attacks.' It does not explicitly mention alternatives, but the context of sibling tools (e.g., unescape_html, strip_markdown) makes the exclusion 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?

The description is consistent with annotations (readOnly, idempotent) and adds context about supported models and the nature of the computation. No contradictions or missing behavioral traits.

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 sentence that efficiently conveys the purpose and scope, with no wasted words. It is front-loaded with the core action.

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 cost estimation tool with three well-described parameters and an output schema (not shown), the description covers all necessary context without redundancy.

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 schema already sufficiently describes each parameter. The description adds no additional semantic nuance beyond the schema definitions, earning a baseline score.

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 estimates API cost in USD for a given model and token counts, with a specific verb and resource. It distinguishes from sibling tools like count_tokens or token_budget_calculator by focusing on cost estimation and listing supported model families.

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 (cost estimation for major models) but does not explicitly state when not to use it or recommend alternatives. However, the name and limited description offer sufficient implicit 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 indicate read-only and idempotent behavior. The description adds that it extracts the 'first valid' JSON, handles specific formats like ```json blocks, and is designed for chaotic output, adding useful 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?

Two sentences: first defines action and input type, second provides usage guidance. No unnecessary words, front-loaded with core 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 simplicity (one parameter, simple extraction), the description fully covers what it does, when to use it, and how input is handled. The presence of an output schema further reduces need for return value details.

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

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

With 100% schema coverage, the baseline is 3. The description adds value by describing the input as 'chaotic LLM output' and detailing the types of surrounding text, which enriches the parameter meaning.

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

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

The description clearly states the tool extracts the first valid JSON object/array from chaotic text, including handling markdown fences and inline JSON. It distinguishes itself from siblings like 'extract_json_path' by focusing on embedded JSON in prose.

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 says to call it 'whenever an LLM returns structured data mixed with explanation text instead of raw JSON'. Provides clear context but does not mention when not to use or alternatives explicitly.

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 readOnly, idempotent, and non-destructive behavior. The description adds that array index access via numeric path segments is supported, which provides some additional behavioral context 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?

The description is a single concise sentence with illustrative examples. It is front-loaded with the core purpose. However, it could be slightly more structured (e.g., listing supported features).

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 simplicity (2 required params, output schema exists), the description covers the essential functionality. It does not detail error handling or edge cases, but for a basic JSON extraction 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?

The input schema has 100% description coverage for both parameters. The description adds example paths for the 'path' parameter, but this is minimal added value since the schema already defines the format.

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 (extract), the resource (JSON string), and the method (dot-notation path). It effectively distinguishes from sibling tools like 'extract_json_from_text' which extracts an entire JSON object from text, and 'json_diff' which compares JSONs.

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 no guidance on when to use this tool versus alternatives. It lacks explicit 'when to use' or 'when not to use' instructions, and does not mention any prerequisites or context for selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds useful behavioral info: 'categorized and deduplicated results'. No contradictions, but could detail output structure or limits.

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, no redundant words. Front-loaded with the core action, followed by output characteristics and use cases. Highly efficient.

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 simple parameters (2, 1 required), complete annotations, and presence of output schema, the description provides all necessary context without gaps.

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 is 3. Description adds default behavior for 'types' parameter ('default: all three'), which goes beyond schema. Provides meaningful extra context.

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 specific verb 'Extract' and resources 'URLs, email addresses, and domain names'. Clearly distinguishes from sibling tools (e.g., url_decode, domain-specific extractors) by specifying the exact types extracted and categorized output.

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 use cases like 'content auditing, link checking, and web scraping validation', giving clear context. However, does not explicitly state when not to use or mention alternatives, missing some 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 the tool as read-only, idempotent, and non-destructive. The description complements this by stating the return data (line numbers, tag types, message text) and its broad applicability, adding value 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 two sentences long, front-loaded with the action, and contains no unnecessary words. Every sentence 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 simple input schema (3 parameters, 1 required) and the presence of an output schema (which explains return values), the description provides sufficient context. The mention of default tag set and return fields completes the picture.

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?

All three parameters have descriptions in the input schema (100% coverage), so the description adds no new information beyond what the schema provides. 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 clearly states the tool's function: extract TODO, FIXME, HACK, etc., tags from source code or text, and specifies what it returns (line numbers, tag types, message text). It differentiates from sibling tools by focusing on technical debt auditing, which is unique among the many text processing 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 for technical debt auditing but does not explicitly specify when to use this tool over alternatives or when not to use it. No exclusions are mentioned, but the context is clear enough for an agent to infer its purpose.

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 agent knows it's safe. The description adds behavioral context: credentials are transient, and children listing is optional. 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?

Three concise sentences with no wasted words. Front-loaded with purpose, then input options, then security note.

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 output schema exists, the description does not need to detail returns. It covers all key aspects: input modes, optional behavior, and security. Complete for a fetch 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?

With 100% schema coverage, the description adds value by clarifying the relationship between page_id and page_url ('accepts a numeric page_id or a full page URL') and explaining include_children ('optionally lists direct child pages').

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 'fetch', the resource 'Confluence page', and the output 'clean Markdown'. It also specifies input options (page_id or URL) and optional behavior (list children). This distinguishes it from sibling tools like create_confluence_page.

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 context for use (fetching, reading) and mentions security ('BYOK...never stored'). However, it does not explicitly state when not to use or name alternatives (e.g., create_confluence_page for writing).

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 discloses important behavioral traits beyond annotations: credentials are handled in-memory only (BYOK), and descriptions are converted to Markdown. These details add significant transparency. No contradictions with annotations (readOnlyHint, destructiveHint).

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 essential information (returned fields) and a critical security note. 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?

Covers core functionality and security adequately. Lacks error handling information (e.g., invalid credentials, issue not found), but the presence of an output schema mitigates the need for full return-value descriptions. Enough for agent decision-making.

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%, and the description does not add parameter-level meaning beyond the schema. The description focuses on output rather than input details, so it meets the baseline without further enhancement.

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 'Fetch' and resource 'Jira issue', listing specific fields returned (summary, status, assignee, etc.). It is distinct from sibling tools like search_jira_issues and post_jira_comment, making the 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?

No explicit usage guidelines or comparisons with alternatives are provided. The purpose is implied for fetching a single issue by key, but there is no directive on when to use this tool versus search_jira_issues or post_jira_comment, leaving room for ambiguity.

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 mark readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. Description adds source list but no additional behavioral traits like rate limits or pagination.

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 focused sentences with no wasted words. Front-loads the action and key sources.

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?

Has output schema (not shown), so return format is documented. With only 2 optional parameters and clear context, the description is sufficient for an agent to understand 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?

Input schema covers both parameters (limit, category) with descriptions and defaults. Schema coverage is 100%, so description adds no extra meaning 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?

Description clearly states verb 'Fetch' and resource 'latest QA & AI/LLM articles' with specific RSS sources (Google Testing Blog, DEV.to, etc.). It distinguishes from sibling tools as a unique feed fetcher.

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?

Description provides clear context: 'Perfect for agents monitoring the QA & AI landscape.' It implicitly suggests when to use, but lacks explicit when-not-to-use or alternatives.

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

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

Annotations provide strong safety signals (readOnly, idempotent, non-destructive). Description adds context about output format options and conversion behavior, adding value 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 concise sentences, front-loaded with key action and scope. Every sentence is informative 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?

Covers purpose and format variations adequately. With an output schema present, return value explanation is unnecessary. Lacks edge cases but sufficient for a simple formatter.

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 has 100% coverage with descriptions for all parameters. Description summarizes the tool's action but does not significantly add meaning beyond the schema. Baseline score 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?

Description clearly states the tool formats few-shot examples for LLM prompts, with specific verb 'Format' and 'Converts'. It also lists supported formats, distinguishing it from related siblings like build_rag_prompt.

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 does not explicitly state when to use or avoid this tool versus alternatives. It implies usage for formatting few-shot examples, but lacks guidance on exclusions or comparisons.

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 agent knows this is a safe read operation. The description adds the search and return behavior context, which is consistent and transparent.

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 plus a list of categories. Every sentence serves a purpose: first defines the tool, second gives usage advice, and the list clarifies categories. 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 has 3 parameters with 100% schema coverage, annotations, and an output schema, the description covers all necessary context: what it does, when to use, how parameters work, and what results to expect.

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 meaning by explaining that 'query' is for keyword search, 'category' is a filter, and 'with_schema' controls whether input schemas are returned. This adds value beyond the schema alone.

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 searches available MCP tools by keyword or category, returning matching names, descriptions, and optionally schemas. This function is unique among the sibling tools, which are all domain-specific operations.

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 advises when to use it ('unsure which tool to use' or 'explore the catalogue') and lists categories. It does not explicitly say when not to use it, but the positive guidance 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?

The description adds significant behavioral context beyond annotations: it discloses that the tool calls an LLM ('calls your LLM to fix'), specifies token cost ('uses ~300-500 tokens'), and notes the requirement for an external API key ('Requires BYOK LLM key'). Annotations already indicate readOnlyHint and other non-destructive properties, so 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?

The description is four sentences, each earning its place: purpose, inputs/action, benefit, and requirement. It is front-loaded with the verb+resource and avoids 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 moderate complexity (4 parameters, LLM call) and the presence of an output schema, the description covers inputs, process, token cost, and prerequisite. It provides enough information for an agent to decide 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%, so the description does not need to add parameter details. However, it adds value by explaining how the parameters map to the tool's workflow: 'Takes the current gherkin text and the _gherkin_warnings array' (matching gherkin and warnings) and notes that it 'calls your LLM' (implying api_key and model). This extra context justifies a 4.

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: 'Fix Gherkin syntax warnings from a jira_to_test_suite result.' It specifies the resource ('Gherkin syntax warnings'), the action ('fix'), and the scope ('ONLY the flagged issues'), distinguishing it from other tools like jira_to_test_suite or full regeneration.

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 context for when to use this tool (after jira_to_test_suite) and highlights a key trade-off: 'Lightweight — uses ~300-500 tokens vs ~5k for a full regeneration.' It also mentions a prerequisite: 'Requires BYOK LLM key.' However, it does not explicitly state when not to use it or list alternative tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by noting custom separator support and the dual mode (flatten/unflatten), but does not contradict 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, concise, and front-loaded with the core purpose and an example. Every sentence adds value without repetition 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?

Given the tool's simplicity, the description covers the essential behavior. The presence of an output schema (stated but not shown) means return values do not need explanation. The description is complete for an agent to use correctly.

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

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

Schema coverage is 100%, so the schema already describes each parameter. The description adds context by explaining the purpose of the separator and the two modes, enhancing understanding beyond the raw 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's purpose: flatten nested JSON to dot-notation or unflatten back. It provides a concrete example and mentions custom separators, leaving no ambiguity. No sibling tool performs this exact 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 implies usage by describing what the tool does, but it does not explicitly state when to use this tool over alternatives like format_json or merge_json. It lacks guidance on choosing between flatten and unflatten modes.

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 and idempotent behavior. The description adds transparency by listing the unit coverage and the two conversion directions (formatting and parsing), which helps the agent understand the tool's scope without conflicting 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 concise at two sentences, front-loading the key actions. No unnecessary words or repetition. 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?

For a tool with two modes, the description covers the essential functionality and units. It does not explain what happens if both parameters are provided, but that is a minor gap. The presence of an output schema reduces the need to describe return values.

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 all parameters with descriptions, and the tool description adds meaning by explaining that 'bytes' is for formatting and 'size_string' is for parsing. It also mentions the 'standard' parameter implicitly by naming SI and IEC. This goes beyond the schema's enum description.

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