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, and it provides none. It says nothing about authorization, cost, latency, side effects, or what the analysis returns.

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

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

It is short, but the brevity reflects under-specification rather than disciplined conciseness. There is no structure, front-loading, or meaningful content to compress.

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 3-required-parameter tool with no annotations and no output schema, the description is completely inadequate. An agent has no way to know what inputs to prepare or what the tool returns.

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

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

All three parameters (frame_energies_db, elapsed_silence_ms, transcript_fragment) have 0% schema description coverage and the description adds no explanation of their meaning, units, or expected formats. The description fully 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 'analyze turn status' merely restates the tool name in lowercase, adding no specificity about what is analyzed (speech turns? silence? conversation state?) or what the analysis produces. It is a tautology rather than a stated purpose.

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

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

There is no indication of when to use this tool versus the siblings (calibrate_acoustic_thresholds, predict_semantic_closure, run_benchmark_turn_detection) or when not to. No context, prerequisites, or alternatives are given.

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

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about side effects, whether calibration mutates state, what permissions are needed, or how the ambient frames are consumed. The four-word description leaves every behavioral question unanswered.

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 text is short, but its brevity reflects under-specification rather than tight writing. It is front-loaded only because there is nothing to front-load.

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

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

A one-parameter tool with no annotations and no output schema still needs to explain the input's meaning and the operation's effect; the description provides neither. It is entirely inadequate 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?

The single required parameter 'ambient_frames_db' has 0% schema description coverage, and the description contributes no meaning about units, expected array length, or how the frames inform calibration. Nothing compensates for the documentation 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 is a verbatim lowercase restatement of the tool name 'calibrate_acoustic_thresholds' and adds no specificity about what is being calibrated or against what. It gives no way to distinguish this from siblings like analyze_turn_status or run_benchmark_turn_detection.

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 this tool should be used, what preconditions apply, or how it differs from the sibling tools. An agent has no basis to choose it over its siblings.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, and it discloses nothing. It does not state what a 'semantic closure' prediction returns, whether it is a classification or score, latency implications, or any side effects.

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 only three words, but this is under-specification rather than conciseness. It is front-loaded only in the trivial sense that there is nothing else to say.

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

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

For a tool with an undocumented required parameter, no annotations, and no output schema, the description is completely inadequate. An agent cannot determine what the tool returns or when to invoke 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% and the sole required parameter, transcript_fragment, is never mentioned in the description. The parameter name is somewhat self-evident as a string input, but the description adds zero meaning about expected content, length, or format.

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

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

The description is a verbatim restatement of the tool name, 'predict semantic closure', with no added explanation of what 'semantic closure' means or what is actually predicted. It gives no way to distinguish this tool from siblings like analyze_turn_status or run_benchmark_turn_detection.

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 the sibling analysis/benchmark tools, nor any stated preconditions. The description provides no context that would route an agent here rather than to analyze_turn_status.

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, and it provides none. It does not say whether the benchmark is read-only, whether it is expensive/long-running, what it measures, or what happens as a result of running it.

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

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

It is short, but this is under-specification rather than conciseness — a single four-word fragment that does not earn its place by conveying any actionable information. There is no front-loaded purpose statement beyond the name itself.

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

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

For a tool with no annotations, no output schema, and no parameter documentation, the description must explain what the benchmark does and what the agent gets back; it does neither. An agent has no basis to decide when or why to invoke 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 for the description to clarify beyond schema; per the scoring baseline for parameterless tools, a 4 is appropriate. No parameter meaning is missing.

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 merely restates the tool name with no added specificity: it does not say what kind of benchmark, what data it runs against, or what 'turn detection' means here. It also fails to distinguish itself from siblings like analyze_turn_status or predict_semantic_closure, all of which operate in the same turn-analysis domain.

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

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

There is no indication of when to use this benchmark versus the sibling diagnostic tools, nor of any prerequisites, inputs, or intended context. At best the word 'benchmark' weakly implies an evaluation/measurement scenario, but nothing is stated explicitly.

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