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

The description discloses the basic behavior of adding two numbers, which is sufficient for such a simple operation. Since no annotations are provided, the description carries the full burden, and it is clear and transparent.

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

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

The description is extremely concise, consisting of a single sentence. Every word is purposeful, and there is no extraneous information.

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

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

For a simple arithmetic tool with an output schema, the description provides the essential information. However, it lacks guidance on edge cases, precision, or return value details, which could be beneficial.

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 description 'Sum two numbers.' does not add any meaning to the parameters a and b beyond the input schema. With 0% schema description coverage, the description should compensate, but it fails to elaborate on what the parameters represent.

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 'Sum two numbers.' clearly states the action (sum) and the resource (two numbers). It distinguishes this tool from sibling arithmetic operations like divide, multiply, and subtract.

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. The description does not mention any conditions, prerequisites, or scenarios where addition is preferred.

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

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

The description mentions 'non-expired', indicating that expired entries are excluded, which is a behavioral detail. However, with no annotations, it lacks disclosure of side effects, performance implications, or whether it's a snapshot. More context would improve transparency.

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 6 words, very concise and directly to the point. No wasted words.

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 description is minimal but covers the essential purpose. However, the tool has an output schema, and the description does not hint at the return format (e.g., JSON object). For a dump tool, specifying the output structure would enhance completeness.

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 input schema has no parameters, so the description does not need to add parameter meaning. Schema coverage is 100% trivially. The description is adequate.

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 clearly states the tool returns the full non-expired blackboard state. The verb 'Return' and resource 'full (non-expired) blackboard state' are specific. It distinguishes from siblings like blackboard_get (single key) and blackboard_query (filtered), and from math 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 guidance on when to use this tool versus alternatives like blackboard_get or blackboard_query. The description only states what it does, without explicitly advising when to use it or when not to.

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 two failure modes (absent or expired) and the return value. For a simple read tool, this is adequate.

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 that efficiently conveys purpose, behavior, and return value. No unnecessary words.

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 tool's simplicity (one parameter, no nested objects, output schema exists), the description covers the key aspects: operation, parameter role, and return value. Minor gaps on key semantics are not critical.

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 description does not add any detail beyond the schema regarding the `key` parameter format, constraints, or examples. With 0% schema description coverage, the description fails to compensate.

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 clearly states the tool reads a single belief by key, which is a specific verb and resource. It distinguishes from siblings like `blackboard_query` (likely multiple beliefs) and `blackboard_set` (write).

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

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

The description implies usage for reading a single belief but provides no explicit guidance on when to use this tool versus alternatives like `blackboard_query`. It mentions return conditions but not context for selection.

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

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

No annotations are available, so the description completely bears the burden of disclosing behavioral traits. It only states that the tool queries beliefs, implying a read operation, but does not reveal side effects, authentication needs, rate limits, or any constraints like what happens if both pattern and tag are specified. The description is minimal.

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, 14-word sentence. It is extremely concise and front-loads the action ('Query beliefs'). Every word contributes to the purpose. No unnecessary information.

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

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

Given the tool's relative simplicity (two optional parameters) and presence of an output schema (which presumably documents return values), the description is moderately complete. However, it fails to address the logical combination of pattern and tag (AND vs OR), which is a contextual gap. The description is adequate but not thorough.

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 schema has 0% description coverage for its two parameters. The description adds some meaning: 'pattern' is a regex applied to key or string value, and 'tag' is a tag to filter by. However, it does not fully clarify the regex format, the meaning of 'key or string value', or the logical relationship between pattern and tag (AND/OR ambiguity). It partially compensates for the missing schema descriptions.

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 clearly states the tool's purpose: query beliefs by regex pattern and/or tag. It specifies the resource (beliefs) and the action (query), and outlines the criteria (regex pattern on key or string value, tag). The tool is distinct from siblings like arithmetic operations and other blackboard actions, though it could explicitly differentiate from blackboard_get/dump.

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 guidance on when to use this tool versus alternatives. For example, it does not explain when to use blackboard_query instead of blackboard_get or blackboard_dump. There are no explicit context cues or exclusions 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?

With no annotations, the description carries full burden for behavioral disclosure. It states that the tool returns a dict with timestamp and provenance, which is helpful. However, it does not explain side effects like overwriting existing keys, nor does it describe parameter behavior (e.g., confidence, tags) beyond the 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?

The description consists of two concise sentences: the first clearly states the purpose, and the second describes the return value. There is no redundant or extraneous information.

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

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

Given the tool's complexity (5 parameters, no schema descriptions, no annotations), the description is incomplete. It fails to explain parameter usage or the semantics of the belief storage, which is critical for correct invocation. The mention of the return dict is a minor plus but insufficient.

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 input schema has 0% description coverage, and the tool description adds no explanations for the five parameters (key, value, source, confidence, tags). An agent has no semantic understanding of what these parameters mean or how to use them beyond the schema structure.

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 the specific verb 'publish' and the noun phrase 'belief to the shared blackboard', which clearly identifies the action and resource. It is easily distinguishable from sibling tools like blackboard_get (retrieve) and arithmetic 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 guidance is provided on when to use this tool versus alternatives such as blackboard_get or blackboard_query. The description only states what the tool does, without indicating prerequisites or appropriate contexts.

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 must fully disclose behavior. It only states that division raises an error if b equals zero, but omits details like return type, precision, or overflow behavior, leaving significant gaps.

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 exceptionally concise with two short sentences covering the core operation and key error condition. Every word is informative with no 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?

Given the existence of an output schema (though not shown), the description is largely complete for a simple arithmetic tool. It covers the operation and main error, though it could briefly note that the result is a number.

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 clarify parameter roles. The phrase 'Divide a by b' explicitly indicates that 'a' is the dividend and 'b' is the divisor, adding critical order information not evident from the schema 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?

The description uses a specific verb 'Divide' and resources 'a' and 'b', clearly defining the operation. It distinguishes from siblings like 'add', 'subtract', and 'multiply' which perform different arithmetic operations.

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 only implies when to use (when division is needed) but provides no explicit context or alternatives. It does not mention when not to use or compare with related 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?

Annotations are absent, so the description carries the full burden for behavioral disclosure. 'Multiply two numbers' accurately describes the core operation, but does not disclose edge cases, error handling, or return behavior. However, for a trivial arithmetic operation, this minimal description is acceptable and not misleading.

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, 'Multiply two numbers,' with no filler words or redundant information. It is effectively front-loaded and maximally 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?

For a simple two-number multiply tool with an existing output schema, the description covers the essential purpose. It lacks details on return values or edge cases, but these are not critical given the tool's triviality and the presence of an output schema.

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

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

Schema description coverage is 0%, so the description must compensate for the lack of parameter details. It confirms that both parameters are numbers and are to be multiplied, but does not explain individual roles. Since multiplication is commutative, the symmetry reduces the need for further clarification.

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 the specific verb 'Multiply' and the resource 'two numbers', which clearly distinguishes it from sibling tools like add, subtract, divide, power, square_root, and percentage. There is no ambiguity about what the tool does.

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 sibling arithmetic operations. The description does not mention any alternatives, exclusions, or context for when multiplication is preferred.

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 explaining behavior. It clearly specifies the order of operands (b from a) but does not disclose additional traits such as return type, edge cases, or side effects. For a simple arithmetic function, this is minimal but adequate; however, it lacks richness beyond the core 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?

The description is a single, focused sentence with no filler words. It communicates the essential operation clearly and efficiently, earning a perfect score for 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?

Given the simplicity of the tool, the explicit input schema, and the presence of an output schema (as indicated by context), the description provides sufficient context. It does not need to explain return values or complex behaviors, making it complete for its purpose.

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

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

Although the input schema has no descriptions for a and b, the description adds critical semantic meaning by clarifying the subtractive relationship: a is the minuend and b is the subtrahend. This compensates for the 0% schema description coverage and helps the agent understand how to correctly pass arguments.

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 clearly states the tool's action as 'Subtract b from a', using a specific verb and resource. This unambiguously identifies the operation and distinguishes it from sibling tools like add, multiply, and divide.

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 for subtraction but does not explicitly state when to choose this tool over alternatives. There is no mention of exclusions or competing tools, leaving the usage context to be inferred from the operation name and sibling context.

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