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

No annotations exist, so the description must carry the behavioral load. It usefully discloses the return content (overview, key specs, numbered steps, validation criteria) and that output is grounded in real framework docs. However, it omits any caveats on plan generation (e.g., does it require a knowledge base to be populated, latency, or that plans may be generic for undocumented frameworks).

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 purpose sentence followed by scope and a bulleted use-case list; the trailing 'Returns:' line is slightly redundant with the earlier 'Returns an actionable plan' sentence but is cheap and skimmable. Overall tight and well-structured.

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

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

With no output schema, the description correctly compensates by enumerating return fields (overview, specs, steps, validation criteria). For a simple 2-parameter generation tool this is close to complete, though the relationship to review_plan in a plan workflow is left implicit.

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 'goal' and 'constraints' are already documented with inline examples. The description adds no syntax, format, or interaction detail beyond what the schema supplies. 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: 'Get a structured implementation plan based on knowledge base documentation.' It clarifies the grounding source (KB docs across ML/AI frameworks) and the output shape. It does not explicitly distinguish itself from the sibling review_plan, which an agent might confuse with plan 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?

Three explicit 'Use this tool when' bullets give clear triggering conditions (about to implement, want doc-informed sequence, want validation criteria). No when-not conditions or named alternatives (e.g., review_plan for critiquing an existing plan) are provided, 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?

With no annotations, the description carries the full behavioral burden. It does disclose the operating model (knowledge-base pattern matching) and the shape of the result (diagnosis, fix steps, prevention advice), which implies a non-destructive read. However, it never explicitly states read-only/non-mutating behavior, whether logs are persisted, or any auth or rate-limit constraints.

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 core action, then a short bullet list of use cases and a returns line. Mostly tight, though the three usage bullets overlap substantially (root cause analysis vs debugging vs understanding behavior), so it is efficient but not maximally compressed.

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, no-output-schema tool, the description usefully explains what is returned (diagnosis, fix steps, prevention advice), compensating for the absent output schema. The remaining gap is the unaddressed safety/read-only profile in the absence of annotations.

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

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

Schema description coverage is 100%, with both required parameters ('symptoms', 'logs') clearly documented in the schema itself. The description only loosely restates them ('checks your symptoms and logs') without adding format, size, or content guidance, so the 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?

The description opens with a specific verb+resource ('Diagnose errors, failures, or unexpected behavior using knowledge base documentation') and clarifies the mechanism (matching symptoms/logs against known failure patterns). It does not, however, differentiate itself from the sibling 'search_knowledge', which also operates over a knowledge base — an agent could reasonably confuse the two.

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 explicit 'Use this tool when you need' contexts: root cause analysis, debugging config/dependency problems, explaining unexpected framework behavior. That is clear when-to-use guidance, but it names no exclusions and does not route the agent away from 'search_knowledge' or toward 'verify_code_math' for adjacent tasks.

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

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

With no annotations, the description carries the full behavioral burden, and it does disclose the return format (markdown) and the failure mode (error if the page ID is not found). It does not mention permission requirements, size limits, or what to do if the ID is unknown (e.g., fall back to search), so it is strong but not exhaustive.

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 core purpose in the first sentence, followed by workflow context and a bullet list. The bullets restate some of the prose above them, which is mildly redundant but still scannable and short overall.

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

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

For a single-parameter lookup tool with no output schema, the description covers purpose, how to source the ID, the trigger conditions, the return format, and the error case. Nothing an agent needs to call it 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% and the schema already gives example IDs, so baseline is 3. The description adds meaning beyond the schema by stressing the ID must be 'exact' and by explaining the primary way to obtain it — from [PageID] citations in other tools' responses.

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

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

States a specific verb (retrieve) and resource (knowledge base page) with its key constraint: lookup by exact ID. It is clearly distinguishable from sibling search_knowledge because it explicitly says it reads a specific page 'without searching'.

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

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

Provides an explicit when-to-use list with three concrete triggers, including the citation-follow-up workflow ([PageID] seen in another tool's output). This tells the agent both when to reach for this tool and what upstream signal selects 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?

With no annotations, the description carries the full burden. It discloses the output shape ('ranked hypotheses with rationale and suggested next steps') and that results are grounded in documentation, but says nothing about side-effect profile (read-only/advisory), determinism, latency, or prerequisites for the knowledge base. Adequate but leaves real gaps for a no-annotation tool.

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

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

Purpose is front-loaded, but the phrase 'backed by documented framework patterns and best practices' appears twice and the bullet list largely restates the opening paragraph. The redundant content costs space without adding new selection signal.

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 no output schema, the description appropriately describes return values (ranked hypotheses, rationale, next steps), and both simple string parameters are covered. For a two-parameter advisory tool this is nearly complete, missing only explicit read-only/advisory confirmation.

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 fully documented in the schema, giving a baseline of 3. The description adds only loose framing ('ranked by fit for your use case') and no format or syntax guidance for current_status or recent_experiments beyond what the schema states.

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: 'Propose ranked approaches or solutions based on knowledge base documentation,' which distinguishes it from retrieval siblings like search_knowledge or get_page. However, it never names or contrasts a specific sibling (e.g., build_plan, diagnose_failure), so the differentiation is inferable rather than explicit.

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

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

Provides clear context with a 'Use this tool when you need' block covering ideation and ranked alternatives. It stops short of exclusions or alternatives (no 'use build_plan instead when...'), so the agent must infer boundaries against the planning/diagnosis siblings.

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

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

No annotations are provided, so the description carries the full disclosure burden. It does describe the return shape ('suggestion table with ranges and KB-grounded justification'), which is valuable given there is no output schema. However, it says nothing about grounding limits, freshness, permissions, or whether results are cached — behavioral traits beyond a bare lookup are undisclosed.

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 core purpose, then a scannable 'Use this tool when' list and a 'Returns:' line. Slightly redundant between the opening sentence and the 'Look up recommended parameter values...' sentence, but every part is short and earned.

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

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

For a single-parameter read tool with no output schema and no annotations, the description covers purpose, usage triggers, and return format, which is close to sufficient. The main residual gap is not distinguishing itself from 'search_knowledge', leaving an ambiguous choice between two documentation-lookup 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% and the schema already supplies free-form query examples, so 3 is the baseline. The description adds meaning above baseline by enumerating what dimensions a good query should encode (hardware, model size, task type, scale), which helps the agent formulate inputs the tool can actually serve.

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 ('Query'/'Look up') plus a concrete resource (documented configuration values, recommended ranges, tuning heuristics for frameworks and libraries). An agent can tell it is a knowledge lookup, not an action tool. It does not, however, differentiate itself from the sibling 'search_knowledge', which appears to overlap in scope.

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

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

The explicit 'Use this tool when you need:' block gives three clear triggering conditions (defaults, ranges/tuning strategies, context-specific suggestions), which is strong positive guidance. It lacks any when-not condition or named alternative, so it falls short of a 5 that would route the agent away from 'search_knowledge' when appropriate.

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?

No annotations are provided, so the description carries the full burden. It does disclose the return shape ('approvals, risks, and improvement suggestions') and that evaluation is against KB documentation, but says nothing about whether the tool is read-only, what happens when the KB lacks coverage, cost/latency, or failure modes.

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

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

Front-loaded one-line purpose, then scannable bullets, then a 'Returns:' line. Every element supports selection or invocation; only mild redundancy between the opening sentence and the bullet list.

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 no output schema and no annotations, the description covers purpose, usage triggers, and the shape of the response, which is enough for an agent to call it correctly. Missing only edge-case behavior (KB misses, read-only guarantee, latency).

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 reinforces 'proposal' ('Pass your proposed approach') but never mentions the required 'goal' parameter, so it adds essentially nothing 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 and resource ('Review your implementation plan against knowledge base documentation') plus scope ('before coding'). The framing of validating an existing plan versus producing one implicitly separates it from build_plan, verify_code_math, and search_knowledge.

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?

Explicit 'Use this tool when you:' block with three concrete conditions (validate a plan before executing, catch wrong framework assumptions, learn pitfalls). Clear positive guidance, but it never names a sibling alternative or an exclusion case (e.g., 'use build_plan if you have no plan yet').

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?

With no annotations, the description carries the full burden and does disclose real behavior: it returns a synthesized answer with [PageID] citations, and it explicitly supports parallel multi-query invocation. It omits rate limits, auth expectations, and any indication of result size or paging, so not fully transparent.

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

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

Front-loaded purpose sentence followed by scoped coverage and a clean bulleted usage list. Some promotional filler ('much faster and more accurate than guessing') could be trimmed, but the structure is easy to scan.

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 search tool with no output schema and no annotations, the description supplies the return format (synthesized answer with citations) and usage timing, which is what an agent needs. Gaps are minor: no pagination or result-scope expectations.

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

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

Schema description coverage is 100% and the schema already gives examples for both query and context, so the baseline is 3. The description reinforces the multi-angle query idea but adds no syntax, format, or constraint 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?

States a specific verb+resource ('Search the knowledge base') and enumerates the covered content domains (framework docs, API references, config formats, best practices). It is clearly distinct from siblings like query_hyperparameter_priors or get_page by domain, though it never names an alternative 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?

Provides a concrete when-to-use list ('Understand how a framework works', 'Look up config formats', 'Get verified information instead of guessing') plus timing guidance ('BEFORE you start coding') and a parallelism tip. No explicit when-not or named alternatives, so it stops short of a 5.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return shape (verdict Pass/Fail plus discrepancy analysis), which is useful. However it says nothing about permissions, whether the check is read-only, latency/cost, or how the knowledge base lookup behaves — gaps for a tool with zero annotation coverage.

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 sentence, followed by a scannable bullet list of use cases and a short Returns line. The only filler is the mildly promotional clause 'catching errors before they become bugs.' Structure is otherwise tight and well ordered.

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 no output schema, the description correctly compensates by stating the return value (Pass/Fail verdict plus discrepancy analysis). For a two-parameter verification tool this covers what an agent needs to invoke it and interpret results. The absence of any behavioral/permission context keeps it from being 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 schema already documents both parameters, including concrete examples for concept_name. The description adds no further syntax, format, or sizing guidance for code_snippet or concept_name. Baseline 3 applies 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 opens with a specific verb+resource: verifying code correctness against knowledge base documentation and reference implementations. That is clearly distinct from sibling tools like search_knowledge or diagnose_failure. It stops short of explicitly naming or excluding any sibling, so it does not reach the top tier.

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 'Use this tool when you need:' block gives three concrete triggering scenarios (verifying concept/algorithm/API implementation, detecting logic errors, comparing to reference implementations). This is clear context for selection. It offers no explicit when-not-to-use guidance and names no alternative tool (e.g. diagnose_failure) for overlapping cases.

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