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

With no annotations, the description carries the full behavioral burden and mostly does not: it does not say whether the advance is persisted, whether it is idempotent, what happens if the step is out of order, or what the response contains. The only behavioral content is the semantic caveat that completion is not mastery, which is useful framing but not operational detail.

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 or repetition. It is well-sized, though the second clause is a semantic caveat rather than the operational content an agent most 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 not be explained, but for a state-mutating tool with zero annotations, zero parameter documentation, and no usage context, the description leaves major gaps about persistence, ordering, and prerequisites.

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 two required parameters, so the description must compensate and does not. It only loosely implies a 'lesson' and a 'step'; there is no explanation of lesson_id format, whether step is 1-indexed or 0-indexed, or valid ranges.

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 ('Mark ... complete') and resource ('a lesson step'), so the agent can distinguish it from siblings like start_lesson or get_learner_progress. It stops short of explicitly naming the alternative tools, but the core action is unambiguous.

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

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

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. The clause about mastery hints at intent but does not tell an agent in what situation to call this rather than record_training_attempt or start_lesson.

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 usefully discloses that unreliable OCR is rejected by default, which explains a key precondition. It still does not describe authentication needs, rate limits, what happens to the rejected image, or the exact output shape and analysis behavior beyond Stockfish being used.

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 short, front-loaded sentences with no obvious filler. The first sentence states the core action and the last two add important constraints efficiently. It could be slightly more structured around when to use it versus alternatives, but it is appropriately sized.

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

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

The tool is moderately complex: image input, Stockfish analysis, seven parameters, and an output schema. The description covers the high-level pipeline and the reliable-OCR default, which are the most important safety behaviors. Given the output schema exists, return values need not be explained, but the zero parameter documentation and lack of alternative guidance leave clear gaps.

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

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

Schema description coverage is 0%, and the description mentions none of the seven parameters. Important semantics for nodes, multipv, orientation, side_to_move, allow_unreliable, and infer_castling_rights are left entirely undocumented. The only implied parameter relationship is that allow_unreliable toggles the rejection behavior, which is too thin for a tool with this many knobs.

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 clear two-part verb+resource: recognize a chessboard image and analyze it with Stockfish. This distinguishes it from recognize_chessboard (recognition only) and analyze_position (analysis of a known position), because it combines both steps. However, it does not explicitly name or contrast those closest 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?

The description gives an implied usage context: axis-aligned screenshots and diagrams. It does not point to alternatives or say when the tool should be avoided, such as for non-axis-aligned photos or when only recognition/analysis is already available. This is enough to infer the intended input class but not a full usage policy.

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 that scores use White's perspective and that nodes is a per-position budget, adding useful behavioral context. However, it omits key traits like expected runtime (likely long-running due to Stockfish), resource consumption, whether it blocks, or how to retrieve results—critical for an analysis tool.

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

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

Two concise sentences with no waste, and the core purpose is front-loaded. The second sentence efficiently clarifies two important semantics.

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

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

The tool has 6 parameters with no schema descriptions, no annotations, and takes a full PGN—a complex operation. The description is far too brief, omitting parameter details, behavioral expectations (runtime, blocking), and distinctions from similar analysis tools. An output schema exists, so return values needn't be explained, but significant gaps remain.

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

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

Schema description coverage is 0%, so the description must compensate. It explains 'nodes' (initial_nodes/critical_nodes) and scoring perspective, but leaves multipv, side, and critical_loss_cp unexplained. With 6 parameters and 0% coverage, the description falls well short of providing needed 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?

The description states a specific verb+resource ('Analyze a complete standard-chess PGN'), which is clear. However, it provides no differentiation from siblings like analyze_position, analyze_positions, or review_game, which likely 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?

There is no explicit guidance on when to use this tool versus alternatives such as analyze_position or evaluate_position. The phrase 'adaptive Stockfish searches' hints at a specific mode but doesn't state prerequisites or exclusions.

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

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It says what is returned but not how the analysis is performed, whether it's purely factual (as 'factual' implies), what permissions or game state it requires, or whether it works on any FEN. The single 'factual' adjective is the only qualifier, and it is vague.

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 that states the return scope precisely: pawn structure types (isolated, doubled, passed) and both sides. No filler, no repetition.

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

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

With an output schema present, the description needn't explain return values, so it is reasonably complete for that dimension. However, given no annotations and no usage guidance, an agent lacks context on when this tool is preferable to other position-analysis tools available in the sibling 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 coverage is 0%, so the schema provides only a parameter name ('fen') with no description. The description does not explain what a FEN is or how the analysis depends on it, and with only one parameter the baseline is 4, but since the description adds no parameter-level meaning beyond what's in the schema, a 3 is appropriate.

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

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

The description states a specific verb ('Return') and resource ('isolated, doubled, and passed-pawn geometry for both sides'), clearly distinguishing it from generic position analyzers like analyze_position or evaluate_position. It's a focused, well-scoped purpose. However, it doesn't explicitly name a sibling alternative or contrast with overlapping tools like inspect_position, keeping it out of 5 territory.

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 guidance on when to use this tool versus other analysis tools such as analyze_position, inspect_position, or evaluate_endgame. The description only states what it returns, leaving the agent to infer the use case (pawn-structure-specific analysis). No preconditions or exclusions are mentioned.

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?

There are no annotations, so the description carries the full behavioral burden. It usefully discloses the evaluation convention (from the side-to-move perspective), which prevents sign errors, but says nothing about compute cost, latency, blocking behavior, or determinism for an engine call with a configurable node budget.

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 terse sentences with no waste; the core operation is front-loaded and the evaluation convention follows 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. Still, given three undocumented parameters and numerous sibling analysis tools, the definition is only minimally sufficient to 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?

Schema description coverage is 0%, so the description must explain all three parameters. It addresses only 'fen' implicitly and says nothing about 'nodes' (search budget) or 'multipv' (number of lines returned), which are non-obvious tuning knobs an agent needs explained.

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

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

States a specific verb (analyze) plus resource (FEN position) and names the engine (Stockfish), so the operation is unambiguous. However, it does not distinguish itself from close siblings such as evaluate_position, analyze_positions, or evaluate_move, leaving the agent to guess which entry point applies.

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

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

The description gives no when-to-use guidance, no prerequisites, and no mention of the many alternative analysis tools (evaluate_position, analyze_positions, evaluate_move). The agent must infer the choice from names alone.

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 full burden. 'Through one warm Stockfish process' implies batch efficiency and shared process state but the description does not disclose cost/rate implications, whether results are deterministic given fixed nodes, ordering guarantees, or failure behavior per position. The White's-perspective score convention is a useful addition.

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 with no waste, front-loading the batch-over-warm-process behavior and the score convention.

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 the 0% param coverage and absence of annotations leave the agent without needed context on array shape, nodes/multipv semantics, or how this batch tool compares to the many sibling analysis endpoints.

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 0%, so the description must compensate. It mentions 'FEN requests' generically but does not explain the 'positions' array structure or the per-item nodes/multipv fields (and defaults) that actually control analysis depth and multi-line output. The White-perspective note maps to output meaning rather than parameters.

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

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

States a specific verb and resource (analyze FEN requests) and notes the batch/warm-process implementation. However, with many sibling analysis tools (analyze_position, evaluate_position, start_analysis_job, analyze_game, start_progressive_analysis), the description gives no differentiation of when this batch tool is preferred over those.

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

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

No explicit when-to-use or when-not-to-use guidance. The 'batch through one warm Stockfish process' hints at a multi-position efficiency use case, but the agent is not told to prefer this over single-position siblings or job-based tools.

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

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

There are no annotations, so the description carries the full behavioral burden. It mentions 'two budgets' and 'retaining full supplied history', which hints at cost and state behavior, but it doesn't name the budgets, state whether the call is synchronous or long-running, or clarify what is returned. For a comparatively expensive multi-search tool, this is a material 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?

A single front-loaded sentence with no wasted words. It is efficient, though the compression is partly why the behavioral and parameter details are absent.

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, zero parameter documentation, and no sibling differentiation, the description is incomplete for a 3-parameter analysis tool with several near-identical 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 all three parameters (fen, move, history) are undocumented in both schema and description. The phrase 'full supplied history' is the only hint about history, but it doesn't explain format, purpose, or when to include it. The description does essentially nothing to compensate for the coverage 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?

The verb 'Verify' and its object 'a candidate' are stated, along with the search method and budgets. However, the description does not differentiate this tool from close siblings like evaluate_move, compare_candidate_moves, or evaluate_position, which an agent would otherwise confuse.

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 explains what the tool does but gives no when-to-use context, no conditions under which this is preferred over evaluate_move or compare_candidate_moves, and no exclusions. The agent is left to infer the divot from the bare 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?

With no annotations, the description carries full burden and does usefully disclose the non-obvious deferred semantics: cancellation occurs at the next search boundary, not immediately, and the active search is bounded. But it omits idempotency, whether partial results are retained, and whether the job_id must reference a running job.

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 filler, and the deferred-cancellation behavior is front-loaded after the core action. Nothing is 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-value explanation is unnecessary, and the deferred-cancel nuance is captured. However, given a dense family of siblings (start/get/resume/cancel_analysis_job, cancel_progressive_analysis), the description leaves routing and the job_id parameter under-resolved.

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 exactly one parameter (job_id) with 0% schema description coverage, so the description must compensate. It says nothing about where job_id comes from or what a valid identifier looks like, leaving the single parameter semantically thin.

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: cancels an analysis job, with a meaningful scope detail ('at its next search boundary'). It does not, however, distinguish itself from the sibling cancel_progressive_analysis, so an agent must infer which cancellable entity each tool targets.

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 cancel a job you started via start_analysis_job. There is no explicit when-to-use, when-not-to-use, or reference to alternatives such as cancel_progressive_analysis or resume_analysis_job.

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?

There are no annotations, so the description carries the full behavioral burden. It does disclose the core effect: queued or running analyses are cancelled and the result becomes stale. However, it omits permissions/ownership requirements, reversibility, idempotency, and error 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?

A single, front-loaded sentence that states the action and effect with zero wasted words. It is appropriately sized for a one-parameter cancellation 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 need not be described. The description covers the core mutation and its visible effect, but for a tool with no annotations it is still missing prerequisites such as how to obtain request_id, permission requirements, and interaction with sibling tools.

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 single required parameter request_id is not explained in the description at all. The description does not say where the request_id comes from or how it relates to start_progressive_analysis or get_progressive_analysis, so it fails to compensate for the empty 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 gives a specific verb ('Cancel'), a precise resource ('progressive analysis'), and scope ('queued or running'), plus the side effect of marking the result stale. It distinguishes itself from the generic cancel_analysis_job via the resource name, but it does not explicitly say how it differs 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?

The eligible states ('queued or running') imply when cancellation is applicable, but the description never states when to choose this over cancel_analysis_job, nor what to do if the request has already completed. No 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?

No annotations are supplied, so the description carries the full burden. It hints that all moves share one 'search profile' for fair comparison, but does not disclose cost/compute intensity, whether the call is synchronous or job-based, permission needs, or what 'profile' means behaviorally.

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 scope constraint ('up to eight') arrives early. It is efficient, though the compression leaves real ambiguity unresolved.

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 elaboration. However, for a four-parameter tool with 0% schema coverage, no annotations, and a non-trivial history/profile model, the one-line description leaves essential invocation details 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 touches two of four parameters indirectly ('up to eight ... moves', 'search profile'), but leaves the required 'fen' string and the 'history' array completely unexplained and gives no hint of valid profile values.

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 — 'analyze and compare ... candidate moves' — with the notable scope detail 'up to eight' and 'under the same search profile'. It is clear what the tool does, but it never distinguishes itself from siblings like assess_candidate_move or evaluate_move, which sound like near-overlapping evaluations.

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 explicit when-to-use or when-not-to-use guidance and no named alternative, despite several sibling tools that evaluate individual moves. The phrase 'compare ... under the same search profile' implies batch comparison, but the agent must infer the selection condition 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?

No annotations are provided, so the description carries the full burden. It does disclose a useful cap ('up to twenty') and the origin of the content, but says nothing about side effects on learner progress, permissions required, idempotency, or what happens if the analysis yields fewer mistakes.

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. Every clause (quantity cap, position-specific, source of the lessons) 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?

An output schema exists so return values need not be documented, and the core purpose is stated. However, with no annotations and 0% schema coverage, the missing 'side' semantics and unstated write behavior leave it only minimally adequate.

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

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

Schema description coverage is 0%, so the description must compensate. It indirectly implies learner_id ('the learner's mistakes') and analysis_id ('saved game analysis'), but the 'side' parameter is entirely unexplained and its expected values are unspecified, leaving a required field ambiguous.

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 (create) and resource (game lessons) and scopes it precisely: up to twenty position-specific lessons sourced from mistakes in a saved game analysis. This source framing distinguishes it from generic lesson tools like start_lesson or create_repertoire_drill, 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?

It implies the tool is used after a saved game analysis exists, but gives no explicit when/when-not guidance and does not mention any sibling alternative such as start_lesson or create_repertoire_drill. The agent must infer the workflow position from the phrase 'saved game analysis'.

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 only discloses one trait: expected moves are hidden from the caller. It says nothing about what gets created, whether it mutates learner state, what happens if the FEN is not in the repertoire, or any auth/permission 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?

A single well-formed sentence with no filler, front-loading the verb and resource. However it is arguably under-specified rather than concise for a state-creating tool with two undocumented parameters, so it sits at minimum viable rather than exemplary.

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, but the description still leaves critical semantics unexplained: what a recall drill is, how fen and learner_id interact, and what committing it does. For a creation tool with no annotations and 0% schema coverage, this is not enough.

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 both parameters. The description does not mention 'fen' or 'learner_id' at all, so an agent gets no help understanding that fen is the drill's starting position or how learner_id scopes the hidden expected moves. This is the main gap, since the schema is bare titles only.

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 ('Create a repertoire recall drill') that clearly marks it as the creation counterpart to get_due_repertoire_drill and submit_repertoire_move. The clause 'without exposing its expected moves' adds a distinctive scope note, though it doesn't explain what a drill actually is or contains.

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 create a drill versus retrieving one (get_due_repertoire_drill) or submitting a move (submit_repertoire_move), and no prerequisites such as whether the FEN must already belong to the learner's imported repertoire.

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 says it 'creates' something ('tutor turn') but does not disclose whether this is persisted, whether it is expensive, whether it requires a prior session, or what state it mutates. For a mutation-style tool with zero annotation coverage, this is a significant 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?

A single sentence, front-loaded, no filler. It is efficient, though perhaps under-specified for the complexity of the tool.

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

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

The tool has 6 parameters, one required, a 0% schema coverage, no annotations, and an output schema. The description is a single one-line sentence that does not cover parameters, usage context, mutation behavior, or relationship to siblings. It is inadequate for a tool of this complexity.

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

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

Schema description coverage is 0% for 6 parameters. The description does not explain any of fen, intent, history, profile, candidates, or learner_id. The only parameter with an obvious meaning is fen, and even that is left implicit. The description fails to compensate for the coverage 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?

States a specific verb+resource: 'Create a grounded tutor turn'. Lists the constituent content (engine evidence, teaching guidance, board actions, speech segments). However it doesn't differentiate from siblings like tutor_move, start_teaching_session, or get_teaching_hint, so it falls 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?

No when-to-use guidance, no alternatives, no prerequisites. The description does not mention when this should be invoked versus tutor_move or get_teaching_hint, leaving the agent to guess.

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 full responsibility for behavioral disclosure. It hints at fallback behavior (using engine analysis when tablebase evidence is unavailable), but says nothing about permissions, safety, rate limits, or output characteristics.

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

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

The description is a single sentence with no wasted words, but it is under-specified rather than concise. The most critical information (what the tool actually does) is not front-loaded or clearly stated.

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 endgame evaluation tool with no annotations and an undocumented parameter, the description is far too sparse. It omits prerequisites, when to use it, and how it relates to sibling tools, leaving major gaps for an 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?

Schema description coverage is 0% and the description does not mention the sole 'fen' parameter at all. It provides no semantic meaning beyond the 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?

The description says to combine endgame features with tablebase evidence or engine analysis, but it never states that the tool evaluates an endgame position or what it returns. It does not distinguish itself from siblings like evaluate_position, analyze_position, or get_tablebase_result.

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 is given on when to use this tool versus the many other evaluation tools. The mention of fallback to engine analysis is internal behavior, not a usage condition.

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 disclosure burden. It usefully states that scores are White-relative, an important interpretation detail, but says nothing about permissions, cost/rate limits, engine requirements, or side effects for what is presumably a read-only analysis 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 short sentences, purpose front-loaded, no filler. Every clause conveys something useful.

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 4-parameter analysis tool with no annotations and zero schema description coverage, two sentences are insufficient. Missing: parameter formats, when to prefer this over sibling evaluators, and any behavioral context (auth, engine cost). The output schema does relieve it of explaining return values.

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

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

Schema description coverage is 0%, so the description must carry parameter meaning. It hints at the 'move' parameter ('a legal move') and obliquely at 'history' ('before and after'), but leaves 'fen', the move notation format, and the 'profile' parameter (default 'quick') completely undocumented.

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 (compare) and resource (a legal move against engine analysis before and after it), which distinguishes it from position-level siblings like evaluate_position. It stops short of naming the alternative tools (compare_candidate_moves, assess_candidate_move) 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?

The description gives no when-to-use guidance or conditions for choosing this tool over evaluate_position, compare_candidate_moves, or assess_candidate_move. The pre/post framing implies a use case but is not spelled out as routing 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?

No annotations are provided, so the description carries the full behavioral burden. It hints that scores are White-relative and that evidence is immutable, but says nothing about cost, latency, permissions, whether the profile trades speed for depth, or how the optional move history affects the result. The output-facing hints overlap with the existing 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?

A single front-loaded sentence with no filler, which is appropriately tight. It is arguably too terse for a 3-parameter tool with zero schema coverage, but there is no wasted wording to trim.

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 explained, but the definition still omits parameter meaning, sibling routing, and any behavioral profile for a compute-style evaluation tool. Given the dense cluster of similar chess-analysis siblings, one sentence is not enough for correct 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 0%, so the description must explain all three parameters and largely does not. It alludes to the optional move history (the 'moves' param), but leaves 'fen' unexplained and gives no indication of what the 'profile' values are or what 'quick' (the default) changes about the evaluation.

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 (analyze/evaluate a position) and adds scope hints about White-relative scores and immutable evidence. However, it does not distinguish itself from near-synonymous siblings such as analyze_position, inspect_position, explain_position, and evaluate_move, leaving the agent to guess which position-scoring tool to call.

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 any alternative tool. In a namespace crowded with analyze_position, analyze_chessboard, inspect_position, and evaluate_move, the absence of routing guidance is a real 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses one behavioral trait (no engine used), but says nothing about whether this is read-only, what computational cost to expect, or what form the inspection output takes. For an analysis tool with zero annotation coverage 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 sentence, front-loaded with the verb and enumerating the covered aspects. No filler.

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

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

An output schema exists, so return values needn't be explained, and one obvious parameter keeps the input side simple. But with no annotations and a crowded sibling cluster, the description should say more about when this engine-free inspection is preferable and how it differs from the many neighboring position-analysis tools.

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 (fen) exists and schema description coverage is 0%, so the description must compensate — but the parameter name 'fen' is self-explanatory to a chess-domain agent and the description correctly frames the input as a position to inspect. Baseline for a single obvious parameter with an output schema present is 4.

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

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

The description states a clear verb (inspect) and enumerates specific aspects (legal state, material, pins, attacks, pawn structures), which is more specific than a generic 'analyze position'. However, it doesn't differentiate from close siblings like inspect_position, analyze_position, or analyze_pawn_structure, which likely overlap significantly.

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 'without an engine' implies a when-to-use condition (engine-free inspection vs engine-based evaluation), but this is oblique. There are many siblings in the analysis cluster (evaluate_position, analyze_position, inspect_position) and the description doesn't explicitly state when to choose this one over 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?

No annotations are provided, so the description carries the full burden. It discloses the data scope ('locally imported') and pagination behavior, but omits matching rules, permissions, rate limits, and return behavior; partial behavioral context only.

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 appropriately sized for a simple search operation.

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

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

The tool is a simple read/search operation and has an output schema, so return details are not required in the description. However, with 0% parameter coverage and many sibling opening tools, the description leaves important query semantics and when-to-use guidance unstated.

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 weakly maps 'opening names' to the query and 'pagination' to offset, but does not explain query matching semantics, case sensitivity, or how offset/pagination behaves.

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'), resource ('opening names'), and scope ('locally imported'), plus pagination. It does not explicitly name sibling alternatives, but the local-import qualifier helps distinguish it from analysis and plan-oriented opening tools.

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

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

The description implies use when searching locally imported opening names, but gives no explicit when-not conditions or alternatives such as get_opening_plans or get_opening_position. Usage is only implied by the tool's narrow scope.

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. 'Immutable' and 'completed' hint that the artifact is final (vs. an in-progress job), but there is nothing about permissions, what happens if the id refers to an unfinished job, or error behavior for unknown ids.

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, though it is arguably under-specified rather than tight.

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 the tool is a simple one-param getter. Still, with no annotations and an undocumented id parameter, the definition falls short of what an agent needs to distinguish it from the many other analysis-retrieval 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?

One parameter with 0% schema description coverage, so the description must compensate and does not: it never explains what analysis_id is, its format, or where to obtain it. The only semantic hint is that it retrieves one analysis/report.

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 (position analysis or completed game report), and the 'immutable'/'completed' qualifiers distinguish it from the live-job siblings. It does not name get_progressive_analysis or get_analysis_job, so an agent must still infer which of the several analysis getters applies.

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 and no exclusion of alternatives, despite many sibling getters (get_progressive_analysis, get_analysis_job, get_analysis_metrics). The agent is left to infer that this is the completed/immutable report fetch rather than a running-job poll.

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 of behavioral disclosure. 'Read' implies a non-mutating operation, but nothing is said about polling cadence, behavior for invalid/expired job IDs, or whether completed-search progress is a partial vs terminal state.

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 compact sentence that front-loads the resource and is free of filler. It is efficient, though the brevity reflects under-specification rather than disciplined trimming.

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 for a job-status reader tied to a start/cancel/resume workflow the description should say how the job is obtained and when polling is appropriate. Those workflow links are absent.

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 job_id has 0% schema description coverage and the description adds nothing about it - not its origin, format, or validity constraints. With low coverage the description should 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 ('Read') and resource ('analysis job status and completed-search progress'), which is more than a restatement of the name. However, it does not distinguish this from close siblings such as get_analysis, get_progressive_analysis, or start/cancel/resume_analysis_job, leaving the agent to guess which job-reader to use.

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 call this versus the sibling analysis tools, and no indication that job_id is expected to come from start_analysis_job or that this is the polling endpoint. Usage is only implied by the tool 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 are provided, so the description carries the full behavioral burden. It states what metrics are reported but does not say whether this is a read-only operation, what permissions are required, whether sampling is continuous or point-in-time, or how the bounded samples are scoped.

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

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

The description is a single front-loaded sentence that names the operation and enumerates the returned metric categories without filler. It is appropriately sized for a zero-argument reporting tool, though the phrase "bounded engine latency samples" is somewhat jargon-heavy.

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 the description need not explain return values, and the zero-parameter schema requires no parameter documentation. However, with no annotations and no usage guidance, the description is only minimally complete for an agent deciding when and why to call this metrics tool.

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

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

The tool takes zero parameters, so there are no argument semantics to explain and the schema is trivially complete. The description appropriately does not introduce parameter details, matching the baseline for a zero-parameter tool.

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

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

The description uses a specific verb ("Report") and lists the exact metric categories it returns: engine latency samples, queue delays, cache hits, and reserved interactive capacity. It does not, however, distinguish this metrics tool from sibling analysis tools such as get_analysis_job or get_progressive_analysis, so an agent must infer the intended monitoring context.

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

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

The description provides no when-to-use guidance, no alternatives, and no conditions for selecting this tool over the many sibling analysis tools. An agent is left to infer that this is for observing analysis engine metrics rather than running or inspecting analysis jobs.

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. 'Report' implies a read-only operation and 'without launching an engine' is a useful behavioral trait, but it does not explicitly state side effects, permissions, or performance characteristics.

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. It immediately states what is reported and the key constraint.

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 zero-parameter capability-reporting tool with an output schema, the description is largely complete: it says what is reported and that no engine is launched. It could be slightly richer on when to choose this over sibling inspection tools, but no critical information is missing.

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

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

The tool has zero parameters and the input schema fully documents this. With no parameters to explain, the baseline score of 4 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 uses a specific verb ('Report') and names the exact resources ('engine settings' and 'exact local corpus counts'), plus a distinguishing constraint ('without launching an engine'). This clearly separates it from sibling analysis, evaluation, and job-control 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?

It gives a clear usage context: call this when you need engine settings or local corpus counts without starting an engine. It does not name alternative tools or state explicit exclusions, but the context is specific enough to guide selection.

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

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

No annotations are provided, so the description carries the full behavioral burden, yet it only restates output fields (attempts, successes, due dates) that the output schema already documents. It never discloses that this is a read-only operation, whether learner authorization is required, or any scoping/rate 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?

A single terse sentence with no filler, front-loaded with the verb and the returned content. It earns its place but is arguably too sparse for the ambiguity it faces.

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 existing output schema and the single param is trivial, so the basics are present. However, with no annotations and several overlapping siblings, the description omits the usage routing an agent needs to invoke this tool correctly rather than get_learner_progress.

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 single parameter (learner_id) is never mentioned in the description. The param name is largely self-explanatory, which keeps this from being worse, but the description does nothing to compensate for the missing coverage (e.g., format, source of the ID).

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 ('Return') and a clear resource set (concept-specific attempts, independent successes, due review dates). It is distinct from a generic progress tool by the word 'concept-specific', but it never names or differentiates itself from the similarly-scoped sibling get_learner_progress.

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 no when-to-use, when-not-to-use, or alternative guidance. With siblings like get_learner_progress and get_due_repertoire_drill clearly overlapping in the learning-progress/review space, the agent has no basis for choosing this tool.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses two non-obvious traits: review is transposition-aware and expected moves are hidden (so the agent must not assume the answer is visible). However, it omits whether selecting a drill mutates review state or advances a schedule, and gives no auth/permission 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?

A single tight sentence with the core action front-loaded and no filler. It is efficient, though the trailing 'hide expected moves' clause could be clearer about whether hiding is a guarantee or a side effect.

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-value explanation is not required. Still, for a stateful training tool with no annotations, the definition leaves key call-time questions open: does invoking it consume or advance a review item, and what prerequisites accompany learner_id.

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 parameter, learner_id, with 0% schema description coverage and no mention in the description. The description adds no meaning about whose repertoire is being drawn from or what a valid learner_id looks like, so the agent must infer it entirely.

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 (select) and resource (a due repertoire position) plus the mechanism (transposition-aware spaced review), which distinguishes it from siblings like get_repertoire_move and create_repertoire_drill. It stops short of explicitly naming an alternative, but the action is unambiguous.

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

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

No guidance on when to call this versus get_repertoire_move, create_repertoire_drill, or submit_repertoire_move, and no prerequisites or sequencing notes. The phrase 'due' implies a scheduling condition but never states what makes a position due or when an agent should invoke this.

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 full behavioral burden. It mentions 'persisted' and 'paginated' but does not disclose permissions, whether preferences are cached, pagination defaults, or rate limits. An agent lacks key operational 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?

Single sentence, front-loaded with the core purpose. Minimal waste, though it tries to combine two concepts without clarity.

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 annotations, an output schema present (so returns need not be described fully), and 0% schema description coverage, the description is insufficient. It should clarify the two data sources, pagination behavior, and parameter usage to be callable 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%, so the description must compensate. It only implies a pagination mechanism via 'paginated' but does not explain the 'offset' parameter's role, default, or the meaning/format of 'learner_id'. Significant 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?

States two distinct actions (reading learner preferences and a paginated training-progress view) but is vague about the resource relationship. No sibling differentiation despite many related learner/training tools in the list (e.g., get_concept_progress, update_learner_profile, record_training_attempt).

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 alternatives such as get_concept_progress or update_learner_profile. No prerequisites, authentication context, or exclusions mentioned.

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 implies a read-only report and adds useful context that theoretical plans and practical statistics are kept distinct in the output, but it never explicitly states read-only safety, permissions, or scope limits.

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

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

A single front-loaded sentence with no redundant phrasing. Every clause (exact, local, by source, theoretical vs practical) carries meaning.

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 with zero parameters the schema/invocation side is trivial. However, the description omits when-to-use routing against the many sibling opening tools, leaving a meaningful gap for 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?

The tool takes zero parameters, so per the rubric baseline is 4. There is nothing for the description to clarify beyond the empty 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 ('Report') and resource ('local opening coverage by source'), which is reasonably distinct from siblings like get_opening_plans or find_opening. It does not name a sibling or explicitly contrast itself, and 'coverage' is left abstract, but an agent can infer this is a coverage-summary read 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, prerequisites, or named alternatives are given. The clause about keeping theoretical plans and practical statistics distinct hints at output content but does not tell the agent when to pick this tool over get_opening_plans, import_opening_statistics, or get_opening_position.

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 discloses only that matching is transposition-based and results are kept apart from statistics; it says nothing about prerequisites (e.g., whether an opening plan must be imported first), error behavior, or result scope.

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 key discriminator (transposition-matched plans vs. game statistics) is stated immediately. It is arguably under-specified rather than verbose, which does not hurt 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. But with no annotations, a 0%-covered required parameter, and no indication of prerequisites or how this differs from the many neighboring opening/analysis tools, the description is not complete enough to call the tool confidently.

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 'fen' has 0% schema description coverage, so the description must compensate — yet it says nothing about the FEN format, whether a full or partial position is accepted, or how transposition matching uses it. The parameter's meaning is left entirely implicit.

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 (opening plans) and adds the qualifier 'transposition-matched,' which distinguishes the result set from generic opening data. It also implicitly separates itself from statistics-oriented siblings, though it does not name any of 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?

The phrase 'separately from practical game statistics' hints that this tool is the alternative to statistics tools, implying when to prefer it. However, no explicit when-to-use condition, prerequisites, or named alternative (e.g., get_opening_coverage, import_opening_statistics) is given.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'source-attributed' and 'transpositions', which hints at behavior, but does not state whether the operation is read-only, what the output contains beyond a generic 'moves' return, whether pagination applies (offset parameter exists), or any rate or permission 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?

The description is a single, compact sentence with no filler. It front-loads the action and the key modifiers. It could be slightly more structured to separate conditions or return behavior, but it is appropriately sized.

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 lack of annotations, 0% schema description coverage, and many sibling tools, the description is not complete enough. It does not explain required FEN syntax, the offset/pagination behavior, or when this tool is preferable to alternatives like find_opening or inspect_position. An agent would need to infer too much.

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 should compensate. It implies the position is specified (presumably via 'fen'), but does not explain the required FEN format or the purpose of the 'offset' parameter. Baseline is 3 when schema descriptions are high, but here the low coverage means the description falls short; however, it does at least convey what the queried entity is.

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 ('Get') and resource ('corpus moves at a position'), and adds distinguishing details like 'source-attributed' and 'including transpositions'. However, it is not sharply differentiated from siblings such as find_opening, evaluate_position, or inspect_position, which also operate on positions. The purpose is clear but lacks explicit sibling differentiation.

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 guidance on when to use this tool versus alternatives. With many sibling tools that analyze, inspect, or find openings, an agent has no indication of which context should select get_opening_position. No exclusions or prerequisites are 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?

No annotations are provided, so the description carries the full burden. It does disclose one important behavioral trait: stale results are withheld, which is valuable beyond schema. But it omits whether the call is read-only, whether it blocks or polls, what a pending vs completed status looks like, or any rate/refresh guidance.

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?

Single compact sentence front-loaded with the verb and resource, then the behavioral caveat. No wasted words. Slightly terse given how much it leaves unsaid, but structurally sound.

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-shape explanation is not required, but the description should still cover when to poll and how request_id is obtained given 0% schema coverage. As a status-polling tool in a dense sibling family with no annotations, it leaves too much unspecified for reliable 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% for the single required parameter 'request_id', so the description should compensate but does not mention it at all. With only one parameter and no enum, the baseline is moderate; the description leaves the identity of the request_id (which start call returns it) entirely to inference.

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 ('Read') and resource ('progressive analysis status') plus a notable behavior ('withholding stale results'). However, it does not distinguish itself from siblings like get_analysis, get_analysis_job, or get_progressive_analysis's natural counterpart cancel_progressive_analysis; it sits in a crowded family of polling/status tools without clarifying its niche.

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 explicit when-to-use or when-not-to-use guidance. An agent cannot infer whether to call this instead of get_analysis_metrics or get_analysis_job, nor when to escalate to start_progressive_analysis. No prerequisites or timing guidance (e.g., poll after start) are provided.

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

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

No annotations are provided, so the description carries the full behavioral burden, and it says almost nothing beyond 'get' implying a read. It never explains what 'personal' means, whose repertoire is consulted, whether an empty position returns an error, or what the offset is for (pagination?).

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

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

A single front-loaded sentence with no filler. It is efficient rather than padded, though the brevity edges into under-specification rather than crisp 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?

The output schema does relieve it of explaining return values, but with no annotations, 0% parameter coverage, and an ambiguous offset argument, the definition is not complete enough for an agent to invoke it confidently in a crowded repertoire-related toolset.

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 for both parameters. 'At a position' loosely implies the fen input, but the offset parameter is left completely unexplained — is it pagination over matching moves, and what are its units? Two undocumented parameters at 0% coverage is a real 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?

States a specific verb and resource ('Get personal repertoire moves') with a scoping qualifier ('at a position'). It clearly contrasts with the mutation sibling submit_repertoire_move, though it does not distinguish itself from get_due_repertoire_drill or the opening-lookup 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?

No when-to-use guidance, no prerequisites, and no alternatives named despite many related siblings (submit_repertoire_move, get_due_repertoire_drill, find_opening, get_opening_position). 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions that segments are 'interruptible' and that pronunciations are legal, but it does not describe side effects, auth requirements, idempotency, or what the plan actually contains.

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. However, it is under-specified rather than appropriately concise for a tool with three parameters and no annotation or schema descriptions.

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 no annotations, 0% input schema description coverage, and no mention of required parameters like 'fen' or 'segments', the description is not complete enough for an agent to invoke the tool correctly.

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

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

Schema description coverage is 0%, and the description does not mention any of the three parameters ('fen', 'moves', 'segments'). The required chess position FEN and segment list are completely undocumented, so the description fails to compensate for the schema 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?

The description gives a specific output ('interruptible speech segments and legal SAN/UCI pronunciations'), which helps identify the tool's role. However, the tool name is 'get_speech_plan' while the description says 'Create', and it never explains what a speech plan is or how it relates to chess positions, leaving the purpose only partially clear.

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 is provided on when to use this tool versus alternatives such as 'synthesize_speech' or other speech/teaching tools. The description implies a use case but does not state conditions, prerequisites, or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses the caveat that the report may be incomplete ('without claiming complete coverage'), which is genuine behavioral context, but it says nothing about read-only status, cost, or what a partial result implies for the caller.

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 tight sentence with the operation front-loaded and the completeness caveat attached economically. No filler.

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

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

With zero parameters and an output schema already covering the return shape, the description needs only to convey intent and scope, which it does. The only shortfall is the absence of any guidance on when to reach for this versus sibling tablebase tools.

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 the baseline is 4. The description adds no parameter detail, but none is needed given the empty 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 states a specific verb ('inspect') and resource ('configured tablebase directories and WDL/DTZ file pairing'), which is concrete and distinguishable from the sibling get_tablebase_result (which returns a result rather than checks configuration). The scope is clear even without naming the sibling it contrasts 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?

There is no when-to-use guidance and no mention of alternatives such as get_tablebase_result or evaluate_endgame. The diagnostic intent ('diagnostics') is implied, but an agent cannot tell from the text when this should be preferred over the other tablebase tools.

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

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

No annotations are provided, so the description carries the full burden. It does disclose two behavioral traits beyond the name — explicit availability reporting (tablebases may not be configured) and draw semantics — which is real value. It stops short of describing failure modes when Syzygy files are absent or the position is out of range.

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 scope leads. It borders on under-specification rather than verbosity, but the structure itself is clean.

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-value detail need not appear in prose, and the operation is low-complexity (one parameter). Still, the absence of any parameter guidance and of usage routing leaves the definition thin for a tool the agent must call with a correctly formatted FEN.

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 (fen) has 0% schema description coverage, and the description adds nothing about its format or expectations. Since FEN is a standard but easily malformed input, this leaves the agent without guidance on valid values.

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 resource (Syzygy WDL/DTZ tablebases) and a verb (probe), so the general purpose is recoverable. However, "probe configured local Syzygy WDL and DTZ" is jargon-heavy and does not clearly state that it returns the tablebase evaluation for a given position, nor does it distinguish itself from the sibling get_tablebase_diagnostics.

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 statement of when to use this tool, when not to, or which sibling to prefer (e.g., get_tablebase_diagnostics or evaluate_endgame). The agent must infer usage 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?

With no annotations, the description carries the full burden. It usefully discloses that the session is 'solution-hidden' and that puzzle selection is rating-matched, but it does not say whether this starts/creates stateful session data, what prerequisites or permissions apply, or what happens if no due puzzle exists.

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

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

A single front-loaded sentence with zero filler: verb, resource, and scope qualifiers in order of importance. Nothing to trim.

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. However, for a stateful session-starting tool with 0% parameter coverage and no annotations, the description omits prerequisites and the meaning of its parameters, leaving real gaps for 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 description coverage is 0%, so the description must compensate for two undocumented parameters. It only gestures at learner context ('the learner's rating') and says nothing about the required learner_id or the optional theme filter, leaving the theme parameter entirely unexplained.

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 gives a concrete verb (select) and resource (a due puzzle) plus scope qualifiers (near the learner's rating, solution-hidden session). It is clear what the tool does, though it never names the sibling it differs from (e.g., get_due_repertoire_drill), and the name 'get_tactic' vs 'puzzle' wording is slightly loose.

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 implies the usage context (training flow, learner is due for a puzzle) but gives no explicit when-to-use/when-not guidance and does not point to alternatives like get_due_repertoire_drill or the follow-up submit_tactic_move. Usage is inferable but not 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?

With no annotations, the description carries the full burden. It does disclose a side effect ('recording assistance') and a progressive constraint ('without exposing the full solution early'), which is meaningful. However it says nothing about session prerequisites, whether hints are consumed/monotonic, or error conditions.

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 well-formed sentence with the reveal action front-loaded and the behavioral caveat trailing. No wasted words, though it is arguably too terse for a zero-annotation, zero-coverage 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 need not be explained. However, with no annotations and 0% parameter coverage, the description should do more to cover session requirements and the semantics of the level range to be fully actionable.

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 adds meaning for 'level' by indicating a range of four progressive hints, but says nothing about 'session_id' (its format, source, or required lifecycle). Partial compensation only.

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 (reveal) and resource (hint), with the scope clarified as 'one of four progressive hint levels'. This clearly distinguishes it from siblings like submit_teaching_move or start_teaching_session, 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?

The phrase 'recording assistance without exposing the full solution early' implies pedagogical usage during a teaching session, but there is no explicit when-to-use, when-not, or named alternative. The usage context is inferable but not 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?

With no annotations, the description carries the full burden, and it does disclose one genuinely useful trait: the imported advice is not engine-verified. But it says nothing about mutation semantics – whether an import overwrites an existing plan, how source_version conflicts are handled, or what permissions are required.

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, front-loaded with the purpose and followed by the one behavioral caveat. No filler, no restatement 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?

An output schema exists so return values need not be explained, but for a mutation tool with no annotations and undocumented nested fields, the description omits conflict/overwrite behavior and prerequisites. It is roughly minimally adequate rather than 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 0%, so the description must compensate. It loosely maps to the source/source_date/source_version fields by saying 'dated, versioned ... with source attribution', but the remaining required fields (fen, name, plans, pawn_breaks, piece_placements, common_mistakes) are given no meaning anywhere.

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 gives a specific verb (import) and resource (dated, versioned opening teaching plan with source attribution), which is clearer than a tautology. However, it does not differentiate from siblings like import_opening_statistics, import_opening_tsv, or import_study, which are also 'import' 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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of why to choose this importer over import_opening_tsv, import_opening_statistics, or import_study. The only contextual hint is the verification caveat, which is not selection 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 carries the full burden. It implies a write operation and hints at provenance ('source-attributed') and population scoping, but never states whether existing statistics are merged or overwritten, whether duplicates are detected, what permissions are needed, or what happens on partial input. For an 8-required-parameter write tool this is a significant 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?

A single front-loaded sentence with no filler or repetition. It is appropriately sized, though its brevity is partly under-specification rather than disciplined economy.

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, but the tool has 8 required parameters with zero schema documentation, no annotations, and no output-schema relief for input semantics. The description does not come close to filling that gap.

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

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

Schema description coverage is 0% across 8 required parameters (fen, move, source, source_date, population, and three win/draw counts), and the description explains none of them. It only gestures at 'source-attributed' and 'population', leaving formats like the FEN string, move notation, and source_date encoding undocumented everywhere.

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 ('Import') and resource ('source-attributed aggregate opening outcomes') with a scope qualifier ('explicitly described game population'). This distinguishes it from sibling writers like import_study and import_opening_plan, though it never explains how it differs from import_opening_tsv, which appears to be the closest analogue.

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 statement of when to use this tool, when not to, or which sibling import path to prefer for a given data format. With four other import_* siblings in the list, the absence of routing guidance is costly.

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 for what is clearly a write operation. It does not disclose permissions needed, idempotency/duplicate handling, batch size limits implied by 'bounded', or what happens to existing opening data on import.

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 tight sentence with no filler, and the core purpose is front-loaded. It is appropriately sized, though the compression is partly responsible for the missing detail noted elsewhere.

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, but for an unannotated mutation tool with 0% parameter coverage the description is thin. It never defines the batch bound, the source format, or the failure/conflict behavior an agent would need before invoking 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 0%, so the description must compensate for two undocumented parameters. It does add real meaning to both: the tsv parameter is a Lichess-format TSV batch and the source parameter is an explicit attribution field, but no column format, delimiter, or expected source values are given.

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 and resource (import an opening TSV batch) plus the source format (Lichess-format), which helps separate it from siblings like import_opening_plan and import_opening_statistics. It stops short of explicitly stating what makes it different from those other import tools, so an agent must infer the distinction from the format word alone.

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 statement about when another import tool is preferable, and no prerequisites. The word 'bounded' hints at a size constraint but the description never states what the bound is or how to comply with it, leaving the agent to guess.

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 that each batch validates the setup move and the completeness of the source line, which is useful, but it omits what happens on validation failure, whether imports are all-or-nothing, size limits implied by 'bounded', and the fact that this is a write operation.

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 front-loaded sentence with the verb and resource first, and no padding. It is appropriately sized even if under-specified.

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 mutation/import tool with no annotations and 0% parameter coverage the description leaves too much unstated: format expectations, failure behavior, batch limits, and how it relates to the other import_* siblings.

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

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

Schema coverage is 0% and neither parameter is described in the schema. The description implies csv_text holds CSV content and references a 'source line', giving marginal meaning, but it never defines the expected CSV columns or what values 'source' accepts, so the coverage gap is not compensated.

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 (import) and resource (puzzle CSV batch), and the name itself separates it from siblings like import_opening_tsv or import_study. However, the description never explicitly contrasts with those alternative importers, so differentiation relies on the tool name and domain nouns rather than stated guidance.

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 word 'bounded' hints that batch size is limited, but there is no when-to-use guidance, no prerequisites, and no mention of the alternative import tools (import_opening_tsv, import_opening_plan, import_study) an agent could pick instead. An agent is left to infer routing 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 are provided, so the description carries the full behavioral burden. It says what is imported, including variations and transpositions, but does not disclose side effects, permissions, duplicate handling, or whether the import creates or overwrites data. For an import/mutation tool, this is a significant 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?

The description is a single, front-loaded sentence with no filler. It is efficient and easy to scan, though its brevity leaves much unsaid for a 4-parameter import 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 need not be described. However, with no annotations and 0% schema description coverage, the description is far too sparse for a 4-parameter import operation. It omits parameter meanings, side effects, and usage context.

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

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

Schema description coverage is 0%, so the description must explain all 4 parameters. It only implicitly touches on 'pgn' and 'source' via 'PGN study' and 'source-attributed', while 'name' and 'is_repertoire' are not addressed. It adds little beyond the parameter titles already present in the schema.

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

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

States a clear verb+resource: importing a PGN study. It adds scope with 'source-attributed' and 'including variations and transpositions', distinguishing it from generic parsing tools. It does not explicitly name sibling alternatives such as import_opening_plan or parse_game, 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?

The description gives no when-to-use guidance, no prerequisites, and no alternatives. It does not say when to choose import_study over parse_game, import_opening_plan, or other import tools. Only the implicit context of importing a PGN study is available.

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 that data is bundled (implying no network fetch) and version-pinned, and that it writes into local storage — a mutation of local state — but it says nothing about idempotency, whether it overwrites existing local data, expected runtime, 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?

A single front-loaded sentence with no filler; the key qualifiers ('bundled', 'version-pinned', 'local storage') are packed efficiently into one clause.

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 no parameters, the definition has little left to explain about return values, so it is structurally adequate for a simple zero-arg seed operation. However, for an initialization tool it omits the when-to-run and repeat-safety information an agent needs before triggering 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?

The tool takes zero parameters, so there is nothing to document beyond what the empty schema already conveys; the description adds the only meaningful context (what corpus is loaded and where). This meets the zero-parameter baseline.

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

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

The description names a specific verb ('Load') and a precise resource ('bundled, version-pinned Lichess named opening corpus'), making the operation unambiguous. It implicitly contrasts with user-supplied import siblings (import_opening_plan, import_opening_tsv) by stressing 'bundled' and 'version-pinned', though it never names 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?

There is no statement of when to call this versus the many other opening-corpus tools, nor any prerequisites such as 'run once before find_opening/get_opening_position'. The agent must infer the initialization role from the verb alone.

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 usefully discloses that the tool performs no engine analysis, which is a meaningful behavioral trait, but it does not explicitly state read-only safety, side effects, or what 'inspect' entails beyond that.

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

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

The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core action and the key constraint.

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

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

Given the simple one-parameter schema and the presence of an output schema, the description is nearly complete: it states the action, the input type, and the no-engine-analysis scope. It could be improved by clarifying what 'inspect' returns, but the output schema likely covers that.

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

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

Schema description coverage is 0%, so the description must compensate for the single 'fen' parameter. It identifies the parameter as a 'FEN position,' which adds some meaning beyond the bare schema name, but gives no format details or examples. This partially compensates but leaves a 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?

The description states a specific verb ('Inspect') and resource ('FEN position'), and distinguishes itself from sibling analysis tools by saying 'without engine analysis.' However, 'inspect' remains somewhat vague about what aspects of the position are examined, so it falls short of the most precise 5-level clarity.

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 'without engine analysis' implies when this tool is appropriate compared to engine-based siblings like evaluate_position or analyze_position, but it does not explicitly name alternatives or state when-not-to-use conditions. Usage is only implied, not fully 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?

No annotations are provided, so the description carries the full burden. It usefully discloses that moves are returned in SAN and UCI notation, but omits ordering, count, and behavior on invalid FEN input, and says nothing about the safety profile of a read operation.

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 tight sentence with the key input and output details front-loaded. Every word earns its place; nothing is padded or 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?

With an output schema present, return values need not be explained, and the description still notes the notation formats. For a one-parameter read tool this is nearly complete, missing only failure-mode behavior.

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

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

Schema coverage is 0% and the single 'fen' parameter is undocumented in the schema. The description only implies the parameter is a FEN string, adding marginal meaning without explaining format specifics or validity requirements.

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 (list) and resource (legal moves) plus the input (FEN position) and output notations (SAN and UCI). An agent can distinguish it from siblings like evaluate_move or get_repertoire_move, though it doesn't explicitly name those alternatives.

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

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

The description says what it does but gives no guidance on when to use it versus siblings such as evaluate_position, inspect_position, or get_repertoire_move. Usage must be inferred from the name and FEN input alone.

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 usefully discloses that no Stockfish is used and that plies are one-based from the supplied starting position, but it does not state whether the operation is read-only, how invalid PGN is handled, or any permission 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?

Two short sentences with no wasted wording. The core purpose is front-loaded and the output indexing detail follows 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?

With an output schema present, the description does not need to explain return values. For a single-parameter parsing tool, it covers the essential purpose, engine exclusion, and ply indexing; only error and format constraints are 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 0%, so the description must compensate for the undocumented 'pgn' parameter. It adds meaningful constraints by specifying 'one standard-chess PGN' and clarifying ply indexing, but it does not explain accepted PGN variants, headers, comments, or invalid-input behavior.

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 gives a specific verb ('Parse'), a clear resource ('standard-chess PGN'), and a distinguishing constraint ('without Stockfish'). An agent can tell this apart from Stockfish-based evaluation or analysis 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?

The phrase 'without Stockfish' implies lightweight parsing rather than engine-backed analysis, which gives some usage context. However, it does not explicitly say when to choose this over siblings like analyze_game, review_game, or import_study, nor does it state exclusions.

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

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

With no annotations, the description carries the burden and does well: it discloses that processing happens locally (no upload), that confidence/reliable/plausible style fields are returned, and warns about unreliable outputs. It omits whether the operation is read-only/idempotent or what happens on failure, 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?

Three tight sentences, front-loaded with the core conversion and its scope, followed by input formats and the reliability caveat. No filler or redundancy.

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

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

An output schema exists, so return values need not be explained, and the description helpfully warns about the confidence/reliability semantics anyway. It is complete for the primary flow but leaves three of four parameters undescribed, which is the main gap.

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

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

Schema coverage is 0% across 4 parameters, so the description must compensate. It richly documents the required 'image' param (local path, file URL, raw base64, base64 data URL) but says nothing about orientation, side_to_move, or infer_castling_rights, leaving three params to enum/default inference alone.

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

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

States a precise verb+resource ('Convert an axis-aligned 2D chessboard screenshot or book diagram to FEN') and adds a distinguishing scope qualifier ('locally'), letting an agent separate it from siblings like analyze_chessboard or inspect_position without opening schemas.

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 input-context guidance (the kinds of images accepted: screenshot or book diagram) and an explicit caution on when NOT to trust the result ('do not silently trust... reliable or plausible field is false'). It stops short of naming an alternative sibling or the condition that routes to analysis tools, so it is not a 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses idempotency tied to attempt identity, which is real behavioral value, but says nothing about permission requirements, whether re-submission mutates existing records, or error behavior on conflicting attempt_ids.

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 tight sentence with the action front-loaded and no filler. Nothing is wasted, though it is arguably too terse given the documentation gaps elsewhere.

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, but with 0% parameter documentation, no annotations, and four required inputs, the definition leaves the agent guessing about argument formats and write semantics for a state-mutating tool.

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

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

Schema coverage is 0% and all four parameters are undocumented. The description maps loosely to was_successful (outcome) and attempt_id (attempt identity), but learner_id and puzzle_id are given no meaning, nor are formats or constraints.

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 (record) and resource (training outcome/attempt), and the idempotency qualifier hints at the distinct semantics versus a generic write. It is reasonably distinguishable, though it never names a sibling it should be preferred over.

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 reported training outcome' implies the caller is passing a known result rather than triggering evaluation, but there is no explicit when-to-use, when-not-to-use, or alternative among the many sibling analysis/lesson tools.

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

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

With no annotations, the description carries the full burden. It discloses the dependence on durable search checkpoints and checkpoint compatibility, which is useful non-obvious behavior, but says nothing about what happens when checkpoints are incompatible, whether resume is idempotent, or whether prior results are preserved or discarded.

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 padding. The precondition (interrupted/failed/cancelled) and the mechanism (compatible durable search checkpoints) are both stated compactly.

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 here, and for a one-parameter tool the description covers the essential state precondition and mechanism. The remaining gap is behavioral detail around checkpoint incompatibility, which would need annotations or a longer description to close.

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, job_id, has 0% schema description coverage and the description adds no format or sourcing guidance for it. The only added meaning is an implicit constraint that the referenced job be in a resumable state, which is marginal over 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 (resume) and resource (game/analysis job) with the exact precondition states (interrupted, failed, cancelled). This inherently separates it from start_analysis_job and cancel_analysis_job, though it never names those siblings 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?

Clear context for when to call it: only for an existing job in an interrupted, failed, or cancelled state, and only with compatible checkpoints. No alternative is named for the incompatible-checkpoint case, but the conditional framing is genuinely actionable.

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 full burden; it does disclose determinism and the absence of prose output, which is genuinely useful. However, it says nothing about the tool being compute/engine-heavy (implied by multipv and node budgets) or about expected latency, so key behavioral traits remain hidden.

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 with the no-prose caveat placed at the end. No wasted words, though it is arguably under-specified rather than optimally concise.

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 for a 6-parameter analysis tool with zero schema coverage and no annotations, the description leaves the entire configuration surface and any cost/latency expectation unexplained.

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 6 parameters, and the description explains none of them. Opaque tuning knobs like multipv, initial_nodes, critical_nodes, and critical_loss_cp are left entirely undefined in both schema and 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?

States a specific verb+resource ('Analyze and classify a complete PGN') and characterizes the output as deterministic review labels with per-player summaries, which separates it from prose-generating lesson tools. It does not explicitly name a sibling, so it stops short of 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 clause 'No prose coaching is generated' implicitly routes the agent away from coaching/tutor tools toward this one for structured review, but there is no explicit when-to-use statement or named alternative. Usage 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose useful async semantics ('persistent, background job'), implying the caller must poll for results, but it never explains what 'bounded' means, whether the job is cancellable, or how the returned job handle is used for retrieval.

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 object of the action appears immediately. It is perhaps too terse for the amount of unstated behavior, but it wastes nothing.

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 covers return values, but the definition omits the job lifecycle (retrieve via get_analysis_job, cancel via cancel_analysis_job) and any usage context, which is a substantial gap for a start-job mutation with zero annotation support.

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 for two undocumented parameters. 'Analysis or review job' faintly hints at the should_review flag, but the required 'pgn' parameter is never explained (format, size, source), leaving the most important input ambiguous.

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 (start) and resource (analysis/review job), and the qualifiers 'bounded, persistent, background' begin to distinguish it from the sibling start_progressive_analysis. However, it never names an alternative, so an agent must infer the split between the two job starters.

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 (e.g. a parsed game or valid PGN), and no pointer to the sibling tools get_analysis_job / cancel_analysis_job that the caller will obviously need next. The description gives no selection criteria among the many analysis siblings.

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