IA-QA — 152 QA & Dev Tools for AI Agents

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered; the description adds real behavioral detail beyond that — the significance test used (Welch t-test at 95%), the rule that `winner` is only named when significant, and the fallback states "no_significant_difference" and "insufficient_samples". This is meaningful outcome-condition disclosure that the annotations cannot express.

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 compact sentences: the first states purpose, the second delivers output list and decision rules. Nothing is redundant and the most decision-relevant detail (the winner/significance logic) 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?

An output schema exists, so enumerating return fields is arguably redundant, but the description adds value by explaining the conditional semantics behind `winner`. Combined with the minimum-sample rule and the statistical method, it covers what an agent needs; only the absence of guidance on sample-size adequacy or non-normal data keeps it from a 5.

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 both nested params are documented, so the baseline is 3. The description adds a constraint the schema omits: at least 2 scores per variant are required, otherwise "insufficient_samples" is returned. That is a genuine semantic addition beyond the structured fields.

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

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

The description opens with a specific verb+resource ("Generate an A/B test report") and scopes it to comparing two prompts or model configurations via score arrays, which separates it from generic comparators like compare_responses. It stops short of naming a sibling alternative explicitly, so it is clear but not maximally differentiating.

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?

Usage is only implied: you supply two score arrays and get a comparison. There is no explicit statement of when to use this over compare_models or compare_responses, nor any exclusion of pre-computed metrics versus raw prompts. An agent must infer the context from the description.

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

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

Goes well beyond the annotations: it discloses the analysis boundary (no data-flow, cannot follow a value across functions or files), how findings are reported (each cites the producing line), and that the response lists what was NOT analysed. The warning that an empty result is not proof of safety is exactly the kind of limitation an agent would otherwise get wrong, and nothing here conflicts with readOnlyHint/idempotentHint.

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?

Front-loaded with the category label followed by the concrete check list, then limitations, then the advisory caveat — a logical order. The middle sentence is long and list-heavy, but every clause is substantive; only minor tightening would help.

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?

Annotations cover read-only/idempotent safety, the schema fully documents all three parameters, and an output schema exists so return shape needn't be explained. What remains — scope of the checks, analytical limits, and the non-verdict of an empty result — is all present, leaving no meaningful gap for an agent deciding whether and how to call 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 description coverage is 100%, so the schema already defines version1, version2 and context, including the note that omitting version1 analyses only the new version. The description adds the concept of a diff between changed code but nothing further about parameter formats or interactions, which is the expected baseline when the schema does the work.

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?

Opens with a specific role ("Pattern-based diff linter") and then enumerates the exact risky shapes it detects in changed code, so an agent knows precisely what class of tool this is. The closing line ("Advisory triage") distinguishes it from a full security scanner, separating it from the static-analysis and security siblings in spirit even if not by name.

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 frames the intended use (advisory triage) and names the alternative class of tool: "use a static analyser for a real security gate." It also warns that an empty result is not a safety verdict, which is actionable usage guidance. It stops short of naming any of the many security-adjacent siblings (shield_analyze, secret_scan, detect_secrets), so an agent still has to infer which one to reach for instead.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so safety is covered. The description adds the meaningful behavioral detail that the output is UTF-8 text, which tells the agent how to interpret the result; it stops short of stating what happens with invalid Base64 or non-UTF-8 binary bytes.

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 what the tool does and followed by when to use it. Every clause earns its place with no restating of the name or redundant framing.

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

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

With an output schema handling return values, rich annotations handling the safety profile, and a fully documented single parameter, the description covers nearly everything needed to invoke it correctly. The remaining minor gap is error behavior on invalid or non-UTF-8 input, which neither the description nor the visible schema addresses.

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?

There is a single required parameter with 100% schema description coverage, so the schema already carries parameter semantics. The description restates only that it is a Base64 string and adds no format or validation guidance beyond the schema — baseline 3.

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

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

States a specific verb and resource ('Decode a Base64 string') plus the exact output interpretation ('back to UTF-8 text'), making the operation unambiguous. The verb contrast with base64_encode and the explicit UTF-8 output type let an agent distinguish it from encode and from text-formatting siblings without opening the schema.

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 second sentence gives concrete usage contexts (API responses, JWT payload claims, config values, attachment data), which is clear guidance on when to reach for this tool. It does not name an alternative or exclusion — notably it overlaps with the decode_jwt sibling for JWT payloads without routing the agent between them.

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, non-destructive, and closed-world, so the safety profile needs no repetition. The description adds the useful fact that input is treated as UTF-8 text rather than raw bytes, but says nothing about padding, URL-safe alphabets, or error behavior on invalid input.

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, zero waste: the purpose is front-loaded and the guidance follows. No restatement of the name or filler.

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

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

For a one-parameter, idempotent, read-only transform with an output schema, the description covers everything an agent needs to select and invoke it. Only edge-case behavior (padding, URL-safe variant, invalid input) is absent, which is minor here.

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 a single parameter at 100% schema coverage, the baseline is 3, and the description earns one point above it by specifying that the string must be UTF-8 and may contain multi-line or special characters — meaningful beyond the schema's terse "Text to encode".

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

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

The description leads with a specific verb+resource ("Encode a UTF-8 string to Base64") that unambiguously contrasts with the base64_decode sibling. It stops short of explicitly naming or contrasting a sibling, which is the only thing separating it from a 5.

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 second sentence gives concrete use cases (embedding data in JSON fields, HTTP headers, data URIs) and the input characteristics that motivate them (binary data, multi-line text, special characters). It provides no exclusions or alternatives (e.g., url_encode for URL contexts), so it falls short of a 5.

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

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is covered. The description still adds genuine context beyond them: 'No API key needed — runs entirely locally' tells the agent there is no network/auth dependency, and the enumerated outputs set expectations for result shape. It stops short of noting cost, runtime, or minimum-variant requirements.

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, front-loaded with the core operation, then the return payload, then the operational note. No filler and nothing buried at the end.

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

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

With an output schema present, return values need not be described, yet the description summarizes them anyway, and the local-execution note closes the auth question. The only gap is that it doesn't state how many variants are needed or how variantId values affect scoring.

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 single parameter is well documented, so the baseline would be 3. The description goes further by explaining how the array must be constructed — one entry per demographic variant drawn from the same prompt template — which is a semantic constraint the schema only partially encodes via minItems.

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

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

States a specific verb and resource ('Analyse a set of LLM responses generated from the same prompt template but with different demographic variants') and enumerates exactly what it returns (bias score, sentiment, Jaccard similarity, verdict). It never distinguishes itself from nearby siblings like toxicity_scan or guardrail_test, which is the only thing keeping it from a 5.

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 precondition that responses must come from the same prompt template with differing demographic variants is implied strongly enough to guide correct input construction. However, there is no explicit statement of when to reach for this tool versus toxicity_scan or guardrail_test, and no exclusions or alternatives are named.

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

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so safety is covered. The description adds genuinely new behavioral context: it returns ranked results with normalized scores, which tells the agent what to expect from the call.

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 tight sentences with the core action front-loaded. The middle sentence is mildly promotional ('industry-standard') but earns its place by orienting the algorithm against known search systems.

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

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

With an output schema present, a fully documented parameter schema, and complete annotations, the description only needs to convey purpose and output character, which it does. Missing only explicit guidance on ordering versus its ranking/similarity siblings.

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

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

Schema description coverage is 100%, so query, documents, b, k1, and top_k are all documented in the schema. The description adds no tuning guidance for b/k1 or explanation of top_k beyond what the schema 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?

States a specific verb and resource: 'Compute BM25 relevance score between a query and one or more documents.' The keyword-based ranking framing implicitly distinguishes it from vector or embedding sibling tools, but no sibling is named explicitly.

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

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

Mentions the algorithm's provenance (Elasticsearch, OpenSearch, Weaviate hybrid search), which hints at context, but never states when to choose this over alternatives such as embedding_similarity, similarity_score, or rag_relevance_rank. Usage is only implied.

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

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so safety is covered. The description then adds real behavioral value beyond that: it discloses that the tool actually performs token budgeting, injects a system instruction, numbers citations, and attributes sources — transformations the agent would otherwise not know happen inside the call.

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, zero waste: the first states what is built and from what, the second enumerates the four processing behaviors. Front-loaded and appropriately sized for the tool's complexity.

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

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

Return values are covered by the output schema and safety by the annotations, so the description only needs to explain the transformation, which it does. Minor gaps: it does not say whether low-score chunks are dropped when the token budget is exceeded, or how chunks are ordered before numbering.

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, but the description's feature list maps onto the optional parameters and adds meaning — 'token budgeting' tells you max_context_tokens actually truncates context, and 'citation numbering' tells you cite_sources changes the output text, not just metadata. It does not clarify how chunk.score is used (ordering/filtering), which is the one semantic gap.

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?

Names a specific verb (assemble) and resource (RAG prompt) plus the exact inputs (retrieved chunks + user query), which clearly separates it from prompt_template_fill or system_prompt_builder. It stops short of explicitly naming a sibling it supersedes, so an agent still has to infer its place among prompt/context 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?

Usage is implied by the phrase 'from retrieved context chunks and a user query' — use it when you already have retrieval results. There is no explicit when-not, no mention of where it sits relative to split_chunks, rag_relevance_rank, or truncate_to_tokens, and no prerequisites stated.

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=false and openWorldHint=false, so the safety profile is fully covered. The description adds the enumerated metrics but says nothing about the return shape or failure modes for very short inputs, which is the only behavior an agent might need here.

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 short sentences, metric list front-loaded, zero filler. The second sentence is a soft usage hint rather than a requirement, so it is slightly expendable but not wasteful.

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?

A single-parameter, read-only utility with annotations and an output schema, so return values need not be described. The description covers purpose and metric set; only edge-case handling (empty/short text) is unstated, which is minor.

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 ('input'), and the schema documents it at 100% coverage with an example. The description adds no syntax, length, or language constraints beyond the schema, so the high-coverage baseline of 3 applies.

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

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

States a specific verb ('Calculate') and resource ('readability scores') and names the four concrete metrics produced (Flesch, Flesch-Kincaid, Coleman-Liau, ARI). This separates it from generic siblings like text_stats or word_frequency without naming them explicitly.

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

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

'Useful for evaluating LLM output quality' gives an implied context but no explicit when-to-use versus siblings such as text_stats, word_frequency, or response_quality_score. The agent must infer the selection criteria.

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=false and closed-world, so the safety profile is covered. The description adds nothing behavioral beyond that: it doesn't say how multi-word or mixed input is tokenized (the schema example passes "helloWorld fooBar"), how non-alphanumeric characters are treated, or what happens for an unknown target case.

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

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

Two sentences, the supported convention list front-loaded, and no filler. Every clause carries information an agent needs.

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?

An output schema exists, so return values need no explanation, and both parameters are fully described in the schema. What's missing is only the tokenization/edge-case behavior for mixed-format input, which is a modest gap for a two-argument text utility.

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 "to" parameter enumerates the accepted values, so the schema does the heavy lifting. The description lists the conventions in human labels (UPPER_SNAKE_CASE, dot.case) that don't exactly match the schema's codes (upper_snake, dot), so it neither adds nor cleanly reinforces parameter semantics.

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

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

States a specific verb (convert) plus the resource (a string) and enumerates the exact naming conventions supported, so the operation is unambiguous. Sibling utilities are unrelated one-shot converters (generate_slug, text_stats), so no sibling discrimination is needed.

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?

"Essential for code generation and refactoring" gives a loose usage context but never says when to use this versus related helpers like generate_slug, nor does it set any preconditions or exclusions. The usage guidance is implied rather than stated.

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

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

Annotations already declare readOnly/idempotent/closed-world, so the safety profile is covered. The description adds real value beyond that by stating the return payload (ratio plus AA/AAA compliance for normal and large text), which tells the agent what to expect from a pure computation.

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, zero filler: the operation first, the return value second. Nothing is repeated from the schema and nothing needs to be trimmed.

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 deterministic two-parameter calculator with a full schema and an output schema, this is essentially complete — input format and output content are both known. Only minor gaps remain (e.g., shorthand hex acceptance, invalid-input behavior), which do not block correct invocation.

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

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

Schema coverage is 100% and both parameters already document hex format with examples. The description mentions 'two colors' but adds no format, ordering, or edge-case detail beyond the schema, 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?

Specific verb and resource: 'Calculate WCAG 2.1 contrast ratio between two colors.' The WCAG framing distinguishes it cleanly from the neighboring color_convert tool, and it also names what comes back.

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?

Usage is implied by the WCAG framing (accessibility check), but the description never states when to prefer this over color_convert or other color utilities, and gives no exclusions or prerequisites.

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, and openWorldHint=false, so safety is covered. The description adds useful behavioral detail by listing accepted input notations, including shorthand #rgb and CSS rgb()/hsl() forms, though it does not discuss invalid-input handling.

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

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

The description is three tight sentences: purpose first, then usage contexts, then accepted formats. Every sentence adds useful information, and there is no repetition 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?

With one fully documented parameter, rich annotations, and an output schema, the description provides everything needed for correct invocation. Return-value behavior can be delegated to the output schema, so no important context is missing.

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

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

Schema coverage is 100%, so the schema already documents the single input parameter and provides examples. The description still adds meaning by enumerating supported notations (#rrggbb, #rgb, rgb(...), hsl(...)), including the #rgb shorthand that the schema examples do not explicitly mention.

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

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

The description states a specific verb and resource: convert a color between HEX, RGB, and HSL. This clearly distinguishes it from other conversion siblings such as number_base_convert or timestamp_convert, since the resource is explicitly 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?

It gives clear when-to-use contexts: translating design tokens between CSS notations, verifying accessibility, and normalizing user input. It does not name alternatives or exclusions, but for this simple utility the intended usage is well specified.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds valuable behavioral context about output content: it reports facts only, names the cheapest and largest context, and avoids subjective recommendations. It does not mention data freshness or error handling, but that is a minor gap given the annotations and output 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?

Two tightly written sentences with zero waste. The core purpose and scope are front-loaded, followed by the key behavioral constraint. Every clause 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 the presence of an output schema, full annotation coverage, and a single well-documented parameter, the description provides everything an agent needs: what is compared, the input range, and the factual (non-recommendation) nature of the output. No critical information is missing.

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

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

Schema description coverage is 100%, so the single 'models' parameter is fully documented in the schema, including the 2-5 constraint and examples. The description reinforces the 2-5 range but adds no new syntax or format details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description states a specific verb ('Put ... side by side') and resource ('AI models'), enumerates the exact attributes compared (price, context window, multimodal, reasoning, provider, pricing page), and scopes the input to 2-5 models. This clearly distinguishes it from single-model siblings like model_info or list_llm_models.

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

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

It gives clear context for use ('run your cases on each to choose') and explicitly states what it will not do ('never a "best model"'), which helps an agent understand its role. However, it does not name specific alternative tools (e.g., model_info for single-model details) or state 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 already establish readOnly/idempotent/non-destructive, and the description adds real context beyond them: the deterministic metric set, that a verdict is returned, that the LLM judge is opt-in and requires BYOK credentials, and that inputs are never re-executed. It does not mention the cost/latency implications of the judge call, which is the main remaining behavioral unknown.

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, front-loaded with what it does, followed by the two optional modes, ending with the key scoping constraint. No filler and no repetition of the name.

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

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

With an output schema present, the description needn't explain return shapes, and it still summarizes them usefully. All 9 parameters are schema-documented, annotations cover the safety profile, and the mode logic is fully explained — nothing an agent needs to invoke this correctly is missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds cross-parameter meaning the schema cannot: reference triggers per-output scoring and picks the closer one, while model+api_key activates a qualitative judge. That conditional wiring is genuinely useful beyond the individual field docs; only check_json and task go unmentioned.

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

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

States a specific verb and resource ("Compare two ALREADY-PRODUCED outputs ... side by side") and enumerates the exact artifacts it produces (cosine, ROUGE-L, Jaccard, deltas, JSON diff, verdict). The closing "No re-execution — you bring the outputs" sharply separates it from execution-based siblings like compare_models.

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?

Clearly lays out the three operating modes: bare comparison, reference-based scoring, and model+api_key LLM judging, so an agent knows which parameter combination selects which behavior. It stops short of naming sibling alternatives (similarity_score, diff_text, analyze_responses) explicitly for the cases where those would be preferable.

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

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered. The description contributes one extra behavioral trait, that it emits truncation-risk warnings, but says nothing about estimation accuracy/approximation, per-message overhead handling, or how the max_output_tokens reservation affects the verdict.

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 tight sentences with the operation and the warning behavior front-loaded; no filler. Minor redundancy in re-specifying the message object shape that the schema already defines.

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

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

With an output schema present, the description need not explain return values, and it correctly omits them. It covers the core call intent, but given three sibling token tools, the missing disambiguation and estimation-caveats keep it from fully 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 the baseline is 3. The description restates the message shape (role/content) already documented in the schema and adds no semantics for max_output_tokens or how model ids affect the estimate; the pointer to list_llm_models lives in the schema, not the description.

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

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

The description names a specific verb and resource set: estimate token usage for a message array against a target model's context window. It clearly conveys the check/fit assessment and adds 'warns about truncation risk'. It stops short of naming the near-duplicate siblings (count_tokens, token_budget_calculator), so an agent must infer the boundary itself.

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?

Use is implied ('check if it fits in the target model's context window') as a pre-flight validation step, but there is no explicit when-to-use/when-not guidance and no mention of how it differs from count_tokens or token_budget_calculator. The agent must disambiguate from three similar token-related siblings unaided.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's obligation is lighter. It adds the diagnostics performed, but says nothing about output shape, scoring scale, or length limits on the messages array beyond what the annotations and output schema already convey.

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 short sentences, front-loaded with the analysis dimensions and followed by the input shape and the use case. No filler, though the trailing marketing sentence ("Essential for chatbot QA") carries less information than the first two.

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

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

With an output schema present, the description need not explain return values, and annotations handle safety. It successfully communicates what is analyzed and what input to supply, so an agent has enough to call it correctly; only sibling differentiation is missing.

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

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

Schema description coverage is 100% and the single parameter is fully documented (messages array with role enum and content). The description restates "[{role, content}]" without adding ordering, size, or formatting constraints beyond the schema. Baseline 3 is appropriate for a one-parameter tool whose schema does the work.

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

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

States a specific verb (Analyze) and resource (multi-turn conversation) and enumerates the four dimensions checked: context retention, topic drift, instruction following, repetition. It is distinguishable from single-response scorers like response_quality_score, though it never names its nearest sibling analyze_responses.

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

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

"Essential for chatbot QA" implies the QA context in which to apply it, but there is no explicit when-to-use/when-not guidance and no routing toward alternatives such as analyze_responses or compare_responses. Usage is only implied.

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 cover read-only, non-destructive, open-world and non-idempotent semantics. The description adds real value beyond them: it discloses that the tool performs a live fetch of the target URL, enumerates the exact attributes and insecure patterns it checks, and explains the non-obvious scoring rule (overall score equals the weakest cookie, with average_score reported separately). It stops short of describing failure behavior, e.g. unreachable URLs or pages that set no cookies.

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 purpose sentence is front-loaded, and the remaining detail is a compact enumeration of inspected attributes, flagged patterns, and output semantics. Every clause carries decision-relevant information; nothing is repeated from the title or schema.

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

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

With an output schema present, the description need not document return shape, yet it still clarifies the most easily misread part of the output (weakest-cookie scoring vs. average). Combined with the enumerated checks and the live-fetch disclosure, an agent has everything needed to call it correctly.

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

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

Schema description coverage is 100% and the single 'url' parameter is fully documented as a full URL to audit with an example. The description adds no format, redirect, or authentication detail beyond what the schema already says, so the 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?

States a specific verb and resource ('Audit the security attributes of cookies set by any URL') and immediately narrows scope to Set-Cookie headers, which cleanly separates it from broad siblings like security_headers_check and web_security_audit. An agent knows exactly what this tool does without opening the schema.

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 scope statement ('cookies set by any URL', inspection of Set-Cookie headers) implies the usage context, but there is no explicit routing guidance such as 'use this instead of security_headers_check when you only care about cookies' or any exclusions. The agent must infer the boundary from the sibling names.

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, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description goes meaningfully beyond that by disclosing the return vocabulary (safe/low/medium/high/critical plus per-origin results) and, critically, warning that "unknown" means nothing was tested and must never be read as "safe" — an anti-misinterpretation note that directly prevents a false negative. It does not explain why idempotentHint=false (results can differ between runs) or mention timing/rate considerations.

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 dense sentences, zero filler, and the core action is front-loaded before the return semantics and the "unknown" caveat. Every sentence carries information an agent needs to act or to interpret output correctly.

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 only two parameters, an output schema present, and annotations covering safety, the description is nearly complete: it explains the scan scope, the risk grading, and the failure-mode interpretation. The remaining gap is disambiguation from cors_checker, which matters for tool 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 both parameters are already documented, including that omitting origin tests multiple origins automatically. The description's phrase "across several origins" and "per-origin results" restates this rather than adding new meaning; 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?

States a specific verb and resource ("Audit a URL for CORS misconfigurations") and enumerates the concrete checks performed: wildcard with credentials, reflected origin, null origin, subdomain bypass, missing Vary. It does not, however, differentiate itself from the very similar sibling cors_checker, leaving the agent to guess which of the two CORS tools to pick.

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?

Usage is implied by the verb and the required url parameter, but there is no explicit when-to-use / when-not guidance and no named alternative. The presence of a near-duplicate sibling (cors_checker) makes this omission more costly than it would otherwise be.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds real behavioral value beyond that by listing exactly which signals the analysis emits (step count, logical flow, backtracking, depth estimate). It does not say anything about thresholds, scoring scale, or what happens with an unstructured trace, but given the annotation coverage that is a tolerable gap.

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

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

Two sentences, front-loaded with the core action, and each sentence earns its place: one states what is analyzed and what is detected, the second gives the target use case. No filler or restated title.

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

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

With an output schema present, the description needn't explain return values, and annotations cover the read-only/idempotent profile; the description fills in the analytical scope well. The remaining shortfall is that it doesn't set expectations about input quality (e.g. that traces without explicit steps are handled differently), but overall an agent has enough to call this correctly.

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

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

Schema description coverage is 100% and both parameters carry their own descriptions, so the baseline of 3 applies. The description never mentions 'expected_conclusion' or the optional comparison behavior it enables, adding essentially nothing about parameters 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?

States a specific verb ('Analyze') and resource ('Chain-of-Thought (CoT) or reasoning trace from an LLM') and enumerates the outputs it produces: step count, logical flow, conclusion presence, backtracking, reasoning depth. It is clear what it does, but it never names a sibling it is distinct from, so the agent must infer the boundary with analyze_responses, response_quality_score, or validate_agent_trajectory.

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 sentence 'Useful for o1/o3/DeepSeek-R1 evaluation' implies a context (reasoning-model traces) but not a decision rule. There is no when-not guidance, no prerequisite (e.g. trace must be a CoT rather than a final answer only), and no routing to or away from overlapping siblings such as analyze_responses or response_quality_score.

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

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds genuinely new behavioral context: the tool is language-aware and understands comment syntax for nine language families, which is why its output differs from naive line counting. It omits only minor details such as auto-detection fallback behavior (though the schema covers that).

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

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

Two sentences, zero filler: the first lists what is measured, the second lists supported inputs. Both are front-loaded and every clause carries 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?

With an output schema present and annotations covering the safety profile, the description needn't explain return values. Parameters are fully documented in schema. The only real gap is disambiguation from text_stats, a same-family sibling with overlapping 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 description coverage is 100%, so baseline is 3. The description adds value by spelling out the supported language families by full name (JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, CSS), which clarifies the abbreviated enum-less hints 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?

Names a specific verb ('Count') and resource ('lines of code') and enumerates exactly what is produced: total, code, comment, blank lines, plus comment density. It is clearly a code-specific line counter, but never distinguishes itself from the sibling text_stats, which an agent could easily confuse it with.

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?

Usage is implied by the domain (use it on source code), but there is no explicit 'use this when' statement or mention of the closest alternative, text_stats. The agent must infer the boundary itself.

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

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety needs no restating. The description adds genuine scope context beyond the annotations: 'standard 5-field cron (minute hour day month weekday)' tells the agent that 6-field/Quartz-style or seconds-prefixed expressions are out of scope and would likely fail.

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, zero padding, with the primary action stated first and the format constraint second. Every clause earns its place and nothing is buried.

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

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

With an output schema present, return-value explanation is unnecessary, and the single required parameter is fully covered. The only minor gap is error behavior for malformed or non-standard expressions and any timezone/Day-of-week numbering assumptions, which would help an agent predict failures.

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 single 'expression' parameter is documented with two examples in the schema itself, so the baseline is 3. The description's field-order parenthetical reinforces the format but adds little the schema and its examples do not already convey.

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

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

States a specific verb and resource ('Parse a cron expression') and names the exact output ('human-readable schedule description'), so the input-to-output transformation is unambiguous. No sibling tool touches cron, so an agent can route to it without inspecting the schema.

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?

Usage is implied by the name and one-line purpose – pass a cron string, get readable text – but the description never says when to reach for this instead of, say, timestamp_convert, nor does it flag any preconditions. For a self-evident pure utility this is adequate but unstated.

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

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is fully covered. The description adds that the tool returns parsed fields, a human-readable description, and upcoming ISO timestamps, but since an output schema exists this is largely redundant rather than genuine extra behavioral context.

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 short sentences, front-loaded with the verb+resource and followed by purpose and return value. Nearly every sentence earns its place, with only minor redundancy given the existing output schema.

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 2-parameter read-only tool with an output schema and full annotation coverage, the description is essentially complete. The one gap is the relationship to the cron_parse sibling, which an agent would have to resolve on its own.

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

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

Schema coverage is 100% – both expression and next_runs_count are documented in the schema with examples and ranges. The description reinforces the 5-field constraint and the next-run preview, but adds no syntax or format information beyond the schema, matching the baseline 3.

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

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

States a specific verb and resource ('Validate a 5-field cron expression') plus two concrete adjacent capabilities (explain the schedule, preview next runs). It is clear what the tool does, but it never distinguishes itself from the sibling cron_parse, which plausibly covers the same parsing/explaining ground.

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?

'Use this to debug cron jobs before they reach production' gives a usage context, so guidance is implied rather than absent. However, there is no when-not guidance, no mention of the cron_parse alternative, and no statement about how to handle 6-field (seconds) expressions besides the implicit rejection.

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 single most important behavioral trait beyond the annotations: the signature is NOT verified, so the output is untrusted inspection data. It also states it reports expiry status and the exact expiry date, giving the agent concrete output expectations. The annotations cover safety (readOnly, non-destructive), so this addition is exactly the value-add layer.

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 tight sentences: the first defines the operation and its semantics, the second gives usage context. No redundant restatement of the name 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?

With an output schema present and full parameter coverage, the description need only convey purpose, the no-verification caveat, and when to reach for it — all of which it does. Nothing required to call this correctly is missing.

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

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

There is one required parameter at 100% schema coverage, with the schema documenting the 'header.payload.signature' format. The description adds no format or syntax detail beyond that, so it neither compensates nor detracts — the 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?

States a specific verb and resource ('Decode a JWT') plus the exact return content (header and payload) and a key scoping caveat ('without verifying the signature'). This clearly separates it from siblings like base64_decode or extract_json_path, which handle unrelated 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?

Explicitly names the use case — inspecting claims (sub, iss, exp, roles) during debugging or auth-provider integration — which tells the agent when this is the right tool. It does not, however, name an alternative for validation/verification scenarios, so the agent must infer that no sibling verifies signatures.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the detection method and the exact set of 15 supported languages, which helps an agent understand capability boundaries. It does not discuss confidence or failure behavior, but that is a minor gap given 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 tight sentences with no wasted language. The core purpose and method come first, followed by the supported-language list, which is a useful scoping detail front-loaded after the main 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?

An output schema exists, so return values need not be explained in the description. The description covers the method, supported languages, and purpose, while annotations and schema cover safety and parameter constraints. An agent has enough to select and call 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?

There is only one parameter, and the schema description coverage is 100%; the schema already explains that 'input' must be text and recommends at least 20 characters. The description adds no parameter-level details beyond what the schema provides, so the 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?

States a specific verb and resource ('Detect the natural language of a text') and adds the method (n-gram frequency analysis and common word markers). It also enumerates the supported language set, which clearly distinguishes it from generic text tools like text_stats or word_frequency.

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

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

The description implies the tool is for language identification and lists supported languages, but it does not explicitly say when to use this versus alternatives such as analyzing readability or word frequency. No exclusions or preconditions are given beyond what the schema already states.

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

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so safety is covered structurally. The description adds genuinely new behavioral context: 'Pure and deterministic' and the enumerated outcome categories (ok, renamed, healable, ambiguous, lost, added, rebound) that signal what the agent should expect back. It doesn't cover edge cases like malformed mapping input, so not a 5.

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 purpose and verdict vocabulary are front-loaded and the input-format sentence earns its place. The trailing install command and devtools URL are useful for capture but lean promotional and add length an agent doesn't strictly need to invoke the tool, keeping it from a 5.

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?

An output schema exists, so return values needn't be spelled out, and the schema fully documents both inputs. The description still adds the decisive missing pieces — determinism, verdict semantics, and how to source mappings — making it complete for an agent to call this correctly.

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

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

Schema description coverage is 100% and both 'before' and 'after' are already documented in the schema, including the elements shape. The description restates the same element shape ({role, name, selector, context?}) rather than adding new meaning, so it earns the high-coverage baseline of 3 rather than more.

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

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

The description states a specific verb and resource ('Diff a baseline page mapping against a current one') and names the output contract ('CI-style verdict: PASS / FIX / BLOCK, plus per-element drift'). The domain specificity (page mappings, selector drift categories) distinguishes it from generic siblings like diff_text and json_diff without needing to name them.

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

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

It tells the agent the inputs needed (two mappings as JSON with 'elements' arrays) and how to obtain them (companion @ia-qa/self-healing package or the browser-console snippet), which is clear usage context. What's missing is an explicit exclusion or alternative — e.g. when to prefer diff_text or analyze_diff_bugs over this tool — leaving some routing inference to the agent.

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

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds meaningful context beyond annotations: the LCS algorithm, line-count output, formatted hunks, and configurable context lines, though it does not mention limitations such as line-oriented rather than word-oriented comparison.

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 tightly written sentences with no filler. It front-loads the core operation and follows immediately with practical use cases.

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 complete schema coverage, an output schema, and clear safety annotations, the description supplies everything needed to select and invoke the tool correctly. The algorithm and return-summary details are useful additions rather than requirements.

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 a as original text, b as modified text, and context as 0–20 with default 3. The description reinforces the context-line range but adds no syntax or edge-case guidance beyond what the schema provides.

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

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

The description states a specific verb and resource: 'Compute a unified line-by-line diff between two text strings' and even names the LCS algorithm. It clearly implies a text-diff use case distinct from structured tools like json_diff, but it does not explicitly name or distinguish itself from that sibling.

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

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

It gives clear usage scenarios — comparing prompt versions, configs, code snippets, or any text where exact changes matter. It does not state when not to use it or name an alternative such as json_diff, so it stops short of full routing 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 readOnly/idempotent/non-destructive/non-open-world, so the safety profile is covered. The description adds meaningful behavioral detail beyond that: quoted values, inline comments, export prefix, escape sequences, and that all key-value pairs are returned. It does not say how malformed lines or duplicate keys are handled, which is the remaining gap.

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, front-loaded with the core action and followed by supported syntax edge cases that genuinely earn their place. The closing usage sentence is the least essential but remains short and non-redundant.

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?

A one-parameter, read-only utility with an output schema and full annotation coverage needs little more than this; the description covers purpose, supported syntax, and output shape at a high level. Only edge-case/error behavior for malformed input is unaddressed.

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 one parameter at 100% schema description coverage, the schema already explains that the input is raw .env file content (with a `cat .env` example). The description confirms the input is whole file content rather than a path, but adds no format/size limits beyond what the schema says, 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?

States a specific verb and resource ('Parse a .env file content into a JSON object') and immediately enumerates the syntax it handles, which pins down the exact transformation. This clearly separates it from format-conversion siblings like yaml_to_json or parse_csv without needing to name them.

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

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

Gives concrete usage contexts ('CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically'), which tells an agent the intended scenario. It stops short of stating when NOT to use it or naming an alternative tool, but for a single-purpose formatter the surrounding context is unambiguous.

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

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, and the description still adds real value: it discloses provenance and freshness (price read daily from the provider's own pricing page) plus the prices_as_of/price_status signals the caller can use to judge staleness. It doesn't mention behavior for unknown or unlisted models, which keeps it out of 5 territory.

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?

One sentence, front-loaded with the core action, with the provenance caveat in a compact parenthetical. Nothing extraneous.

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

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

With an output schema covering return fields and annotations covering the safety profile, the description supplies what's missing: provenance and freshness of prices. Only the edge case of an unrecognized model is unaddressed.

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 a 3 is the baseline, and the description earns an extra point by explaining where the `model` value must come from (list_llm_models) rather than leaving it as an arbitrary string. Token parameters are left entirely to 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?

States a specific verb+resource ("Estimate the API cost in USD") with the exact inputs that drive it (model + token counts), which cleanly separates it from token-counting siblings like count_tokens or token_budget_calculator. It also names the model catalog sibling, so the agent knows where valid model names come from.

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?

"Any model from list_llm_models" gives a clear prerequisite/discovery path and the pricing-source note sets expectations about the numbers returned. It stops short of explicit when-not guidance or a direct comparison against token_budget_calculator, so no full 5.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds useful path-syntax context (array index via numeric segments) but omits the most behaviorally relevant fact: behavior on missing paths, type coercion, or how a JSON string input is parsed versus an object input. With annotations carrying the safety burden, this lands at a moderate 3.

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 tight sentences, front-loaded with the core action and followed by the two syntax variants that matter most. Nothing is wasted.

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 two-parameter read tool with a full schema and an output schema, the description covers what an agent needs to invoke it correctly. The only real omission is missing-path/edge-case behavior, which keeps it short of a 5.

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

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

Schema description coverage is 100%, so both parameters are already documented, and the description's path examples duplicate the schema's own example. No additional semantics (escaping, edge cases, root path) are provided beyond the structured fields.

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

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

States a specific verb+resource ('Extract a value from a JSON string') and immediately distinguishes itself from JSON-reshaping siblings like flatten_json or format_json by scoping to path-based traversal. The example paths make the operation 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?

Usage is implied by the examples rather than stated. It never says when to prefer this over flatten_json or json_to_csv, nor what happens if the path doesn't resolve (error vs null). Adequate but with a clear 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 the tool as read-only, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds useful behavioral context beyond annotations by stating that results are 'categorized and deduplicated.' It does not disclose extraction edge cases, but the output schema likely carries return-shape 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?

Three short sentences with zero waste: purpose first, return behavior second, use cases third. The description is front-loaded and appropriately sized for a simple extraction tool.

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

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

For a two-parameter, read-only extraction tool with full schema coverage, annotations, and an output schema, the description gives enough to call it correctly. It covers what is extracted, how results are shaped, and typical uses.

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

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

Schema description coverage is 100%, with both 'input' and 'types' documented, so the schema already does the heavy lifting. The description lists the extractable types, which aligns with the enum, but adds no syntax, default, or filtering guidance 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 begins with a specific verb and resource set: 'Extract all URLs, email addresses, and domain names from text.' This clearly distinguishes the tool from validation siblings like validate_url or validate_email, and from extract_json_from_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?

It names concrete use cases ('content auditing, link checking, and web scraping validation'), giving the agent clear context for when this tool is appropriate. It stops short of explicitly naming exclusions or sibling alternatives, but the intended usage 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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that it works on 'any source code or text' and what the result contains, but says nothing about scanning limits, custom-tag merge behavior, or context truncation beyond what the schema hints at.

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 tight sentences: capability first, return shape second, motivation last. Nothing is redundant or buried.

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

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

With an output schema present and 100% parameter coverage, the description only needs to frame the capability, which it does. Minor gap: it lists the tag set but omits XXX, which the schema lists as part of the default set.

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 applies; the description merely restates that custom tags are supported and that line text is returned. It adds no syntax, case-sensitivity, or default-set detail 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?

Specific verb 'Extract' plus the exact tag set and the return payload (line numbers, tag types, message text). It is clearly distinguishable from nearby tools like count_code_lines or detect_secrets, though it never names a sibling to route against.

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?

'Essential for technical debt auditing' implies the use case, but there is no explicit when-to-use/when-not guidance and no named alternative (e.g. detect_secrets for secrets, count_code_lines for metrics). The agent must infer the boundary itself.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, so the safety profile is covered. The description adds genuinely new behavioral context beyond the annotations: credentials transit in-memory only and are never stored (BYOK), which matters for an agent handling secrets. It still omits failure behavior (auth errors, rate limits, missing page).

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 short sentences with zero waste: purpose and output format first, then input modes, then the optional flag, then the credential-handling caveat. Every sentence adds information and nothing is buried.

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

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

With an output schema present, the description needn't explain return structure, and it still notes the Markdown format for the page body. All required auth parameters are accounted for by the schema and reinforced by the BYOK sentence. The only gap is handling of the optional child listing's output shape and error paths.

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 six parameters including the page_id/page_url alternatives and include_children default. The description restates the page_id-or-URL choice and the optional child listing without adding format details (e.g. that page_id must be numeric, or how to extract the ID from a URL). Baseline 3 applies when the schema carries the load.

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 names a specific verb+resource ("Fetch a Confluence page") and adds the return format ("clean Markdown"), plus the two input modes (page_id or URL). It is unambiguous about what the call does, but never differentiates itself from the sibling create_confluence_page or fetch_jira_issue, so an agent must infer the fetch-vs-create split.

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?

"Accepts a numeric page_id or a full page URL" and "Optionally lists direct child pages" imply when the flags apply, but there is no explicit when-to-use statement, no prerequisite guidance (credentials are required, not optional), and no mention of alternatives such as using a different tool to create or search pages.

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

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

Annotations already declare readOnly=true, destructive=false, idempotent=false, openWorld=true, so the safety profile is covered. The description adds genuinely new context the annotations cannot convey: the BYOK credential model ('transit in-memory only, never stored') and the fact that descriptions are transformed to Markdown on return. It stops short of covering error behavior for invalid keys or missing permissions, which keeps it from a 5.

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

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

Two sentences, front-loaded with the payload contents and followed by the security posture that an agent needs before handling credentials. No filler and nothing buried.

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?

An output schema exists, so return values need no explanation, and the description covers content plus the non-obvious BYOK auth requirement for a 4-required-parameter tool. The remaining hole is failure/edge behavior (invalid or inaccessible issue key) for an open-world remote fetch.

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% — every one of the 7 parameters, including the token/auth trio and the fields filter, is documented inline. The description restates the optional comment and attachment behavior but adds no defaults, formats, or constraints beyond the schema, so the baseline of 3 is appropriate.

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

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

It names a specific verb and resource ('Fetch a complete Jira issue') and enumerates exactly what comes back: summary, Markdown-converted description, status, assignee, priority, labels, custom fields, plus optional comments and attachment metadata. What's missing is any explicit differentiation from siblings like search_jira_issues — an agent must infer the single-issue-by-key vs. search distinction from the field 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?

The description never states when to reach for this tool versus search_jira_issues, nor does it tell the agent to obtain an issue_key via search first. The only implicit usage signal is the phrase 'optionally comments and attachment metadata', which hints at the two boolean flags rather than providing routing 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?

The tool is inherently a transformation tool, and the annotations confirm it is safe (readOnly, non-destructive). The description does not introduce new behavioral constraints (e.g., token limits or specific failure modes), but it is consistent with the safety profile provided by 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 definition is extremely concise. The two sentences are front-loaded and efficient, providing the function and the key features without unnecessary jargon or padding.

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

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

Given that the complexity of the tool is low (it is a formatter), the description, combined with the 100% schema coverage and safety annotations, provides a complete mental model for an agent to select and invoke the tool correctly.

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

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

While the schema provides the structure, the description provides crucial semantic meaning to the 'format' and 'label' parameters by defining the distinct styles supported (e.g., '(User/Assistant)' for chat mode). This elevates the input understanding beyond simple type definitions.

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 is highly specific, clearly stating the tool's function: 'Format few-shot examples for LLM prompts.' It immediately distinguishes itself by listing the supported outputs (chat, XML, Markdown, plain text), which sets it apart from generic formatting siblings like json_to_yaml.

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 LLM prompting but lacks explicit guidance on *when* this is superior to other prompt-building tools (like prompt_template_fill or build_rag_prompt). It is functionally clear but lacks comparative advice.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), but the description adds material context the annotations do not: the 90-day query logging, screening for credentials and personal data, and the fact that inputSchemas are only returned on request. It does not state error behavior beyond what the schema notes about refused categories, which keeps it from a 5.

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 dense sentences, front-loaded with the action and the return shape, then the privacy note, then the category list. Nothing is padded, though the logging sentence interrupts the flow between returns and categories.

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?

An output schema exists, so return structure need not be described; all four parameters are documented at 100% and the category vocabulary is supplied in the description. Combined with annotations covering safety and the added privacy disclosure, an agent has everything needed to call this 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 baseline is 3, but the description earns an extra point by enumerating the twelve valid category values that the schema only refers to as "one of the categories above" (enums count is zero). It otherwise restates defaults already documented per-parameter.

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

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

States a specific verb and resource -- search MCP tools -- plus the scope of the search (keyword or category) and what it returns (names, descriptions, optionally inputSchemas). An agent can distinguish it from siblings like rate_tool or model_info without opening the schema, since it is the only discovery tool in the 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?

"Search available MCP tools ... before calling them" gives clear situational context: use it as a discovery step prior to invocation. There are no explicit exclusions or named alternatives, so it sits at 4 rather than 5.

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

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

Annotations declare readOnlyHint=true and openWorldHint=true; the description corroborates by disclosing the external LLM call, the BYOK key requirement, and a concrete cost estimate (~300-500 tokens), plus the return ('corrected Gherkin'). It does not describe failure behavior (invalid key, unfixable warnings) or whether output is validated after fixing.

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 front-loaded sentences: what it does, the constraint on scope ('ONLY the flagged issues'), then the cost and key prerequisite. No filler and no repetition of field-level details already in the schema.

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, a full output schema, and 100% parameter coverage already in structured fields, the description covers the remaining agent-relevant context: provenance of inputs, scope of the fix, cost, and the BYOK prerequisite. Minor omission: what happens when warnings is empty or the key is invalid.

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 each parameter is already documented, including BYOK key formats and model/provider pairing. The description only restates that gherkin and warnings come from the jira_to_test_suite result, adding no format or edge-case detail beyond the schema — the 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?

States a specific verb+resource ('Fix Gherkin syntax warnings') and anchors it to a named upstream artifact ('from a jira_to_test_suite result'). An agent can tell this apart from jira_to_test_suite and generate_test_cases: this is a targeted repair pass, not 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?

Explains it consumes the _gherkin_warnings array from a prior jira_to_test_suite call and fixes ONLY flagged issues, implicitly steering the agent away from full regeneration ('~300-500 tokens vs ~5k for a full regeneration'). It does not name the regeneration sibling explicitly or state when fixing is not appropriate, so it falls short of a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds the transformation semantics (dot-notation, custom separators), but is silent on edge cases beyond the schema example: whether array indices become keys, how key collisions are resolved, or what happens on invalid JSON input.

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, zero waste: the core transformation and example come first, with unflatten and custom separators trailing as qualifications. No boilerplate or repetition of schema text.

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

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

With an output schema present and 100% parameter coverage, the description need not explain return values, and it correctly covers both modes. The only shortfall is unstated behavior for arrays/collisions and error handling, which matters for a JSON transform that receives arbitrary shapes.

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 mode, input, and separator are already fully documented in the schema, including defaults. The description only restates the separator support and implies the mode switch, adding no syntax or edge-case meaning beyond the structured fields. 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?

States a specific verb (flatten/unflatten) and resource (nested JSON object) with an inline example transformation, and covers both directions in one definition. An agent can immediately tell this apart from format_json, extract_json_path, or json_to_csv without opening any schema.

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

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

Gives clear conditions for each direction: nested input → dot-notation keys, dot-notation input → nested object. It does not name an alternative sibling (e.g., when to prefer format_json for pretty-printing), so there are no explicit exclusions, but the mode selection context is unambiguous.

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

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds the meaningful semantic distinction of SI vs IEC bases, but says nothing about precedence when both 'bytes' and 'size_string' are supplied, or about the 'both' default behavior.

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

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

Two sentences, no filler, capability and unit scope front-loaded. Every clause carries information the agent needs.

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?

An output schema exists so return values need no explanation, and all three parameters are documented. The one gap is the relationship between the 'bytes' and 'size_string' inputs, which an agent must guess.

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, but the description genuinely adds value the schema lacks: it defines what 'si' and 'iec' actually mean (KB=1000 vs KiB=1024) and enumerates the supported unit suffixes, which the enum alone does not explain.

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

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

States a specific bidirectional capability: convert byte counts to human-readable sizes AND parse size strings back to bytes, naming the exact unit families covered. An agent can immediately distinguish this from siblings like number_base_convert or format_table.

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

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

The phrase 'or parse size strings back to bytes' implies the two modes (bytes input vs size_string input), but there is no explicit statement of when to use which, that the two input paths are mutually exclusive, or how this differs from related conversion tools. Usage is inferable but not guided.

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

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered; the description goes further by disclosing that columns are auto-detected, headers aligned, and missing keys rendered as empty cells — defaults an agent could not infer from the schema. No detail on how nested or non-object values are handled, but for a pure transform that is minor.

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, zero padding: the operation and its automatic behaviors come first, the usage trigger second. Every clause carries actionable 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?

With an output schema present and annotations covering safety, the description needs only purpose, behavior, and usage — all present. A small gap remains around edge-case handling (non-object array elements, nested values), which matters for a formatting tool handling arbitrary JSON.

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%, including the 'default: all keys from first row' note for columns, so the baseline of 3 applies. The description adds only the missing-key fill behavior and nothing new about the input/columns parameters themselves.

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

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

States a specific verb+resource ('Convert a JSON array of objects into a Markdown table') and adds concrete rendering behavior (column detection, header alignment, empty-cell filling). It is effectively distinguishable from siblings like json_to_csv or html_to_markdown, but never names or explicitly contrasts an alternative.

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?

'Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document' gives a clear triggering context with concrete examples. No when-not-to-use clause or named alternative is provided, so it falls short of a 5.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds useful behavioral context by enumerating the accepted input dialects (OpenAI `parameters`, MCP `inputSchema`, Anthropic `input_schema`, plain required/optional args) and call shapes, though it says nothing about failure reporting (which the output schema covers).

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?

Purpose is front-loaded in the first clause, and every subsequent clause adds distinct information about accepted formats. It is a single dense run-on sentence, which hurts scannability slightly but wastes no 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?

With an output schema present, both parameters fully described, and the accepted definition/call dialects enumerated, an agent has everything needed to invoke this correctly. Nothing material is missing.

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

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

Schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by spelling out the alternative shapes each definition and call object may take, which materially helps an agent construct a valid `allowed_functions` entry or unwrap a `{function:{…}}` call.

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

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

States a specific verb (validate) and resource (LLM function call / tool_use output), then enumerates exactly what is checked: name in allowed list, argument types/enums/ranges, extra/missing args. This clearly separates it from siblings like validate_mcp_response and validate_agent_trajectory.

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