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 reveals only that a record is created; it says nothing about uniqueness of leetcode_id, duplicate handling, required permissions, or whether an existing entry is overwritten.

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

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

A single short sentence that is front-loaded and wastes no space, but it contains a typo ('inn') and is too thin to be considered well-structured for a 4-parameter mutation 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?

An output schema exists so return values needn't be explained, but for a write operation with no annotations and zero parameter documentation, the definition leaves out the behavioral and input details an agent needs 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 0% across 4 parameters, and the description adds no meaning for leetcode_id, title, difficulty, or topic (e.g., allowed difficulty values, whether topic is free text). With a low-coverage schema the description must compensate, and it does not.

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 ('Add') and resource ('leetcode problem') into a named database, so the agent knows the operation. It does not distinguish itself from siblings like search_problem or log_attempt, but the create-vs-query distinction is implicit.

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

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

No indication of when to use this tool versus alternatives such as search_problem, nor any prerequisite (e.g., check for duplicates first). Usage must be inferred entirely from the name.

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 provided, so the description carries the full burden. It only says 'retrieve complete attempt history' — no mention of permissions, pagination, ordering, or what 'complete' means. Minimal behavioral disclosure.

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 efficient sentence that front-loads the verb and resource. No waste, though it could be slightly more informative without being verbose.

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 explained. However, with no annotations and no usage differentiation from siblings, the description is incomplete for an agent to know when to select this tool over alternatives.

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 0% for the single leetcode_id parameter, but the parameter is intuitive (an integer identifying the problem) and the description implies its role. Baseline for 1 parameter without schema docs is moderate; description doesn't add format constraints but the parameter is self-evident.

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 (complete attempt history for a Leetcode Problem). Clear enough that an agent knows what it does, though it doesn't distinguish itself from the sibling get_recent_attempts.

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

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

No guidance on when to use this tool versus get_recent_attempts or other attempt-history siblings. The description implies full history vs. recent, but doesn't make that distinction explicit.

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

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

With no annotations, the description carries the full behavioral burden. It hints that results are pre-aggregated (saving the agent computation), which is a genuine behavioral trait, but says nothing about scope (all attempts vs recent), permissions, cost, or freshness of the aggregation.

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 first sentence is front-loaded and efficient. The second sentence is a soft justification rather than actionable content, and its value is marginal, making the description mildly padded.

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. For a one-parameter read tool this is close to adequate, but the aggregation scope (which attempts, what time window) remains ambiguous, which is the main thing an agent needs 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?

The single parameter leetcode_id has 0% schema description coverage, so the description must compensate and largely does not. Only the phrase 'for a Leetcode Problem' weakly implies that the id identifies a problem; no format, validity, or lookup behavior is described.

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

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

States a specific verb and resource: 'Return the aggregated performance statistics for a Leetcode Problem'. This is clearly distinguishable from raw-log siblings like get_attempt_history or log_attempt. It does not, however, name any sibling explicitly, so the differentiation is left to inference.

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?

There is no when-to-use guidance: nothing says when aggregated stats are preferable to get_attempt_history or get_weak_areas. The second sentence offers a rationale ('LLM doesn't need to recieve and manually calculate everything') rather than a selection condition, and contains 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?

No annotations are provided, so the description carries the entire behavioral burden. It does not say how 'recent' is bounded, whether results are capped, whether authentication is required, or how the limit interacts with the default of 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?

A single compact sentence with no filler and the resource front-loaded. It is efficient, though its brevity is partly under-specification rather than disciplined conciseness.

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, and this is a simple one-parameter read. However the limit semantics and the meaning of 'recent' are left entirely unstated, which is a real gap for an agent choosing a value.

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?

One parameter (limit) with 0% schema description coverage, and the description never mentions it. The phrase 'most recent' hints at bounded recency but gives no syntax, range, or default behavior, so the schema's own default=5 is the only guidance available.

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 ('the user's most recent LeetCode attempts'), so the agent knows exactly what comes back. It does not explicitly distinguish itself from the close sibling get_attempt_history, which weakens it slightly against the 5 bar.

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

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

No indication of when to prefer this over get_attempt_history or search_interview_memory, no prerequisites, and no exclusions. The agent must guess the boundary between 'recent' and full history.

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 not state whether results are paginated, sorted, time-bounded, or whether a missing topic errors or returns empty. For a read tool with a single required param this is thin.

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 clean sentence, front-loaded with the verb and resource. No waste, though it is arguably too short for a tool whose parameter semantics are opaque.

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?

Output schema exists, so return values needn't be explained, but with zero annotation coverage and 0% parameter description coverage the definition is missing the context needed to call it correctly among topic-similar 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 0% and the description says only 'a specific topic'. Four siblings all deal with topics or attempts, and the description gives no syntax, allowed values, or matching semantics (exact vs substring, known topic list) beyond the bare parameter name.

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?

Clear verb+resource: retrieves the user's attempts filtered by topic. It is distinguishable from get_attempt_history and get_recent_attempts at a high level, though the description doesn't explicitly contrast with those siblings.

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

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

No when-to-use guidance, no prerequisites, and no indication of how this differs from get_attempt_history, get_recent_attempts, or get_problem_stats. The agent must infer the use case.

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 discloses that the tool derives a 'weakness score' from attempt history, which is real behavioral context beyond the schema. It does not state that the operation is read-only, whether scoring is deterministic or recency-weighted, or what happens with an empty attempt history.

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

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

A single front-loaded sentence with no filler; the analytical intent arrives immediately. Slightly loose phrasing ('assign a weakness score to it') costs a little precision but 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 parameterless read with an output schema, the description supplies the essential framing: inputs come from attempt history and the output is per-topic weakness scores. Missing is any hint about scoring scale or how many topics are surfaced, but the return structure is covered by the output schema.

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

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

The tool takes zero parameters, so there is nothing for the description to clarify; the baseline of 4 applies. No parameter-level gaps exist.

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 derived output: 'Analyze the user's attempt history' and 'identify DSA topics where the user appears to be struggling and assign a weakness score.' This is a concrete analytical product rather than a restatement of the name. It does not, however, explicitly contrast itself with siblings like get_attempt_history or get_topic_history that also operate on attempt data.

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 framing ('analyze the user's attempt history') but there is no explicit when-to-use, no prerequisites (e.g. requires logged attempts), and no named alternative such as get_attempt_history for raw data versus this diagnostic view. The agent can infer the intent but gets no 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?

No annotations are provided, so the description must disclose behavioral traits. It does not state side effects, return format, or whether this is a read-only operation, leaving the agent with almost no behavioral context beyond the vague phrase 'say hello'.

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

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

The description is a single, front-loaded sentence with no wasted words, appropriately sized for a trivial 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?

Given zero parameters and the presence of an output schema, the description only needs to convey purpose. It does so minimally, but the utility of the tool remains ambiguous (e.g., is it a test endpoint or a greeting?), leaving a gap for the agent.

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

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

The tool has zero parameters, so the baseline score is 4. The description does not need to explain parameters, and the schema is fully covered.

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 'Say hello from the Interview Coach' essentially restates the tool name 'hello' without specifying what the tool actually does, such as returning a greeting string or performing a health check. It does not distinguish this tool from any sibling tool.

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?

There is no indication of when to use this tool versus the many siblings listed, nor any exclusions or prerequisites. The description provides no contextual guidance for selection.

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

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

With no annotations provided, the description carries the full behavioral burden, yet it only implies a write action. It never states whether logging is idempotent, whether an existing attempt is overwritten, what permissions are needed, or what happens to unset optional fields.

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?

It is a single front-loaded sentence with no wasted clauses, which is appropriate in size. But the all-caps styling and the stray apostrophe in "LOG'S" hurt readability and professionalism without adding emphasis value.

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

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

For an 8-parameter mutation tool with no annotations, the description is far too thin; the agent is given none of the field semantics or write-behavior context. The existence of an output schema reduces the need to explain return values, but that is the only mitigating factor.

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 are 8 parameters with 0% schema description coverage, and the description adds no meaning for any of them—not leetcode_id, solved, confidence, time_taken, or the free-text fields. It neither clarifies types/ranges nor explains which fields matter for which outcome.

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 (logs) and resource (a user's attempt at a LeetCode problem), which is clearer than a mere restatement of the name. However, it does nothing to distinguish itself from read-oriented siblings such as get_attempt_history or get_recent_attempts, so it stops short of 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternative tools for viewing past attempts. The agent must infer entirely from the name that this is the write path while its siblings are the read paths.

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 does disclose a meaningful behavioral trait — retrieval is semantic rather than keyword-based — but says nothing about whose data is searched, permissions, result scope, or how many items are returned by default.

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

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

A single front-loaded sentence with no filler. It is efficient, though the brevity is partly under-specification rather than true density.

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, but with zero annotation coverage and 0% parameter description coverage the definition leaves key gaps: query format, top_k meaning, and whether results are the user's own history or aggregated. Not complete enough for confident 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 description coverage is 0%, so the description must compensate and does not. It never explains what form the 'query' string should take (a topic, a question, a company name) or what 'top_k' controls, leaving both parameters semantically opaque.

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 (search) and resource (the user's previous interview experiences), plus the matching mechanism (semantic similarity). It is distinguishable from siblings like search_problem by resource, but it never explicitly contrasts itself with 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?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as search_problem or get_attempt_history. The resource name implies a use case but nothing steers the agent between this and its 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 burden. It does not state that this is a read-only lookup, whether matching is exact or substring, whether it paginates, or what happens when no problem matches. Only the bare search behavior is disclosed.

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

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

A single front-loaded sentence with no filler; the resource and both accepted query forms are stated immediately.

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 described, and for a one-parameter search tool the description covers the essentials. It is slightly thin on matching semantics and no-result behavior, but nothing essential 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 0%, so the description must compensate. It does add real meaning by stating the query accepts a title or a LeetCode ID, but it never clarifies whether the query is exact-match, fuzzy, or case-sensitive, nor what happens with ambiguous input.

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 (search) plus the resource (LeetCode problems) and the two lookup keys (title or LeetCode ID). It is clear what the tool does, though it does not distinguish itself from the sibling search_interview_memory, which is also a search tool.

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

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

No when-to-use guidance, no prerequisites, and no mention of alternatives such as search_interview_memory or get_problem_stats. The agent must infer that this is the lookup step before add_problem or get_problem_stats.

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